Computer-aided simulation tool for providing assistance in the planning of thermotherapy

ABSTRACT

The invention relates to a computer-aided simulation tool, in particular to computer-aided simulation methods, for providing assistance in the planning of thermotherapy, and to suitably configured computer equipment. The thermotherapy comprises hyperthermic treatment of a tumor volume within a volume of a human body. The hyperthermic treatment comprises the application of a magnetic field within a treatment volume by means of a magnetic field applicator. In at least one depot volume, thermal energy can be introduced by means of magnetic, paramagnetic and/or superparamagnetic nanoparticles deposited in the body, by power absorption in the applied magnetic field. Field strength values and optionally calculated temperature distributions are provided for assisting the user in the planning of the thermotherapy.

CROSS REFERENCE TO RELATED APPLICATIONS

The present application is a National Stage of International ApplicationNo. PCT/EP2012/001034 filed on Mar. 8, 2012, which claims the benefit ofEuropean Patent Application No. 11001993.2 filed on Mar. 10, 2011, theentire disclosures of which are incorporated herein by reference.

The invention relates to a computer-aided simulation tool for providingassistance in the planning of thermotherapy, and to be more precise to acomputer-aided simulation method for providing assistance inthermotherapy planning and to computer devices of appropriate design.

PRIOR ART

Gneveckow et al., “Description and characterization of the novelhyperthermia- and thermoablation-system MFH® 300F for clinical magneticfluid hyperthermia”, Med. Phys. 31(6), June 2004, 1444 ff., describe,inter alia, the metrological determination of characteristic curveswhich indicate the relationship between the values of the magnetic fieldstrength (“H field strength values” in kiloamps per meter, kA/m) and ofthe power absorption rates based on iron mass SAR_(fe) (“SpecificAbsorption Rate of Iron”, in watts per gram, W/g) for particularmagnetic fluids. Further aspects of hyperthermia treatment on the basisof magnetic fluids are described (abstract; pages 1445-1446, section“II. Methods”; page 1447, section “IIIB. Power Absorption” inconjunction with FIG. 5; page 1448, section “IIID. Thermal distributionin the quasi-steady state”; pages 1449-1450, section “IV. Discussion”).

Wust et al., “Magnetic nanoparticles for interstitialthermotherapy—feasibility, tolerance and achieved temperatures”, Int. J.Hyperthermia, December 2006, 22(8), 673-685, describe a concept forhyperthermia treatment on the basis of magnetic fluids. The magneticfluid contains iron oxide nanoparticles, dispersed in water. Themagnetic fluid needs to be distributed in the tumor and is subsequentlyheated by the application of a magnetic alternating field by means of anapplicator. Resultant temperature distributions are analyzed. Resultantvalues for the specific absorption rate in tissue (SAR) are ascertainedfrom the particle distribution determined by means of computedtomography (CT) in combination with specific H field strength values inkA/m. The temperature distribution in the tumor area is calculatednumerically by using a bioheat transfer equation (subsequently BHTE).The calculated temperature distribution is matched to directly measuredtemperature values at reference points in or close to the target area bymeans of a suitably chosen, average perfusion rate. (Abstract; page 675,section “Magnetic fluid”; page 677, section “Post-implantation analysis(PIA) and Thermotherapy”).

Maier-Hauff et al., “Efficacy and safety of intratumoral thermotherapyusing magnetic iron-oxide nanoparticles combined with external beamradiotherapy on patients with recurrent glioblastoma multiforme”, J.Neurooncol. DOI 10/2007/s11060-010-0389-0, published online on Sep. 16,2010, describe intratumoral thermotherapy methods using magneticnanoparticles. A density of the nanoparticles after introduction isascertained by means of CT methods. On the basis of the densitydistribution of the nanoparticles, the SAR thereof and an estimatedperfusion rate within the tumor area, the generation of heat within thetarget tissue is determined as a function of a magnetic field strengthby using a BHTE. The magnetic field strength (subsequently also called“H field strength” or just “field strength”) should be chosen such thata temperature of 43° C. is not exceeded outside a border of 2 cm aroundthe tumor. During treatments, direct temperature measurements were takenin a thermometry catheter positioned in advance. This monitors theobservance of the limit temperature (abstract; page 3, FIG. 1 andleft-hand column and right-hand column, 1^(st) paragraph).

Nadobny et al., “Evaluation of MR-Induced Hot Spots for DifferentTemporal SAR Modes Using a Time-Dependent Finite Difference Method WithExplicit Temperature Gradient Treatment”, IEEE Transactions onBiomedical Engineering, Vol. 54, No. 10, October 2007, pages 1837 ff.,describes the numerical solution to a nonlinear BHTE in the time domain,which contains a temperature-dependent perfusion, and this is taken asthe basis for performing numerical simulations for the temperaturedistribution in the human body (abstract; page 1837, section “I.Introduction”; page 1838, section “II. Time-Dependent BHTE”; pages1840-1841, section IIIC: Thermal Simulation Procedures”; pages1845-1846, sections “V. Discussion”, “VI. Conclusion”). Inter alia,Nadobny et al. described a method (called a “decomposition way”)according to which the BHTE is numerically solved by using the finitedifferences (FD) method to split the temperature distribution into abasal and an SAR-dependent part (page 1841, left-hand column, equations5a, 5b and 6).

GENERAL ILLUSTRATION OF THE INVENTION

It is an object of the present invention to propose a tool for preparingthermotherapy which provides a doctor or other medical personnel with acomprehensive overview of different therapy options in order tofacilitate a decision in favor of a particular option.

This object is achieved by an inventive computer-aided simulation toolfor providing assistance in the planning of thermotherapy, and to bemore precise by an inventive computer-aided simulation method forproviding assistance in thermotherapy planning and also by computerdevices of appropriate design.

The invention proposes a computer-aided simulation method for providingassistance in thermotherapy planning. The thermotherapy comprises(local/regional) hyperthermia treatment of a tumor volume in the bodyvolume of a human body. The hyperthermia treatment comprises theapplication of a magnetic field in the treatment volume by means of amagnetic field applicator. In this case, thermal energy can beintroduced into at least one “deposit volume”, i.e. into a volume whichcontains nanoparticles, by power absorption in the applied magneticfield—by means of magnetic, paramagnetic and/or superparamagneticnano-particles deposited in the body beforehand.

Accordingly, hyperthermia treatment within the context of the presentinvention is used generally to denote therapy by means of raisedtemperature. In the case of relatively small temperature rises up toapproximately 45° C., this results in an increase in the effectivenessof chemotherapy and/or radiotherapy, hyperthermia in the narrower sense(Hildebrandt, B., et al. (2002). “The cellular and molecular basis ofhyperthermia.” Critical Reviews in Oncology Hematology 43(1): 33-56). Inthe case of temperature rises to more than 45° C., (tumor) cells die offdirectly, called thermoablation (Jordan, A., et al. (2006). “The effectof thermotherapy using magnetic nanoparticles on rat malignant glioma.”J. Neurooncol. 78(1): 7-14. Epub 2005 Nov. 29).

It is desirable for the deposit volume or the deposit volumes to besituated fully or at least in part within the treatment volume,preferably the tumor volume. This should have been ensured by thepreceding instillation of the nanoparticles. The tumor volume or thetumor volumes should be situated fully or in part in the treatmentvolume, according to the aim of therapy. Hence, the treatment volume isthe volume which is meant to be heated by the hyperthermia primarily.Whether and to what extent this aim can probably be achieved ispredicted by the inventive simulation method.

The inventive method is considered to be a simulation method forproviding assistance in the planning and—optionally—control ofthermotherapy, because it simulates the temperature distributions in thebody which arise during treatment with the magnetic field applicatorwithout such treatment actually being performed. The method thusrequires no calibration, for example, by measured values which aremeasured for a treatment that is currently taking place. The resultswhich are delivered by the inventive simulation tool can be used to drawconclusions about treatment parameters for a subsequent actualtreatment, e.g. in relation to the field strength value or H fieldstrength value that needs to be chosen.

The user of the inventive method or simulation tool may be a doctor,e.g. a radiologist or a radiotherapist, personnel with technical and/ormedical training who have been trained to use a magnetic fieldapplicator, or other users.

The nanoparticles may generally be particles which interact in some waywith a magnetic field such that (following instillation of the particlesinto the body) tissue is heated. By way of example, they may bemagnetic, paramagnetic or superparamagnetic nanoparticles, e.g. ironoxide nanoparticles with or without a coating. The particles may haveany spatial forms, and may be at least essentially spherical,spheroidal, ellipsoidal, prismoidal or parallelepipedal in shape, forexample. Although the particles are called ‘nanoparticles’ here, thisterm is meant to cover not only particles having dimensions (e.g.smallest or largest radius or diameter) in the nanometer range but alsoparticles having other dimensions, for example particles havingdimensions in the micron range. Preference is given to iron oxidenanoparticles having a diameter (determined under an electronmicroscope) of up to 100 nm.

The inventive simulation method comprises the following steps: in afirst calculation step, also called “T selection” (“T” stands fortemperature), calculation of a field strength value or H field strengthvalue that needs to be set on the applicator on the basis of a geometricdistribution of the nanoparticles and at least one prescribedtemperature limit value which is not meant to be exceeded by thehyperthermia treatment; in an optional second calculation step, alsocalled “H controller” (“H” stands for magnetic field strength) or “fastH controller” depending on the embodiment, calculation of a (resultant)temperature distribution that is to be expected for at least some of thebody volume for each H field strength value from a plurality ofprescribed H field strength values, and/or a user-defined H fieldstrength value, preferably, in an optional third calculation step (“Hselection”), automatic calculation of a temperature distribution that isto be expected for a maximum admissible H field strength value when theH field strength value calculated in the first calculation step isgreater than this maximum admissible H field strength value; andprovision (for the user) of the calculated H field strength value thatneeds to be set on the applicator and optionally of at least onecalculated resultant temperature distribution, e.g. that associated withthe aforementioned H field strength value, in order to provideassistance for the user in planning the thermotherapy.

In particular variants of the inventive simulation method, theprescribed temperature limit value or one of a plurality of prescribedtemperature limit values relates to a maximum temperature or atemperature maximum only within the treatment volume that is to beheated. By way of example, the prescribed temperature limit value mayrelate to a temperature maximum in a range from 60° C. to 100° C.,preferably 70° C. to 90° C., particularly 80° C., in the treatmentvolume.

In addition or as an alternative, the prescribed temperature limit valueor one of a plurality of prescribed temperature limit values may relateto a temperature maximum outside the treatment volume that is to beheated. By way of example, the prescribed temperature limit value mayrelate to a temperature maximum in a range from 40° C. to 45° C.,particularly 43° C., outside the treatment volume.

In some embodiments of the inventive simulation method, two prescribedtemperature limit values which each relate to different volumes are usedin the first calculation step. By way of example, one prescribedtemperature limit value within the treatment volume may relate to atemperature maximum of 80° C. and another prescribed temperature limitvalue outside the treatment volume may relate to a temperature maximumof 43° C. In this case, the two prescribed temperature limit values canpreferably be used simultaneously in the first calculation step.

A third calculation step (“H selection”) may involve a temperaturedistribution that is to be expected being calculated for an H fieldstrength value that is defined as maximum admissible. The H fieldstrength value defined as maximum admissible may relate to an H fieldstrength value that can be set as a maximum on the applicator, forexample, or to an H field strength value for a patient that is definedas maximum admissible. The third calculation step can, on the basis ofthe calculation result of the first calculation step (T selection), beperformed automatically when the H field strength value calculated inthe first calculation step is greater than the H field strength valuedefined as maximum admissible.

In particular variants of the inventive simulation method, notemperature limit value is used in the calculations in the secondcalculation step (H controller; fast H controller) and/or in the thirdcalculation step (H selection). During these calculations, the possiblyresultant simulated temperature distributions may therefore exceed thelimit value(s) considered in the first calculation step. For example,temperatures higher than 43° C. may thus occur outside the treatmentvolume and/or temperatures higher than 80° C. may thus occur inside thetreatment volume. This allows the doctor to have a more comprehensiveoverview of the effects of the planned thermal therapy than if he hadonly strictly temperature-limit-value-based results for the calculationfrom the first calculation step.

The calculations in the second calculation step can be performed for aplurality of prescribed H field strength values that can be set on theapplicator, preferably between 3 and 20 H field strength values,particularly between 5 and 10 H field strength values. In addition or asan alternative, the calculations in the second calculation step can beperformed for a plurality of prescribed H field strength values that canbe set on the applicator, preferably between 3 kA/m and 20 kA/m,particularly preferably between 5 kA/m and 10 kA/m (H controller).

The second calculation step (H controller; fast H controller) can beinitiated after the first calculation step (T selection) and possiblythe third calculation step (H selection) only by a user input. If theuser requires no further orientation after the output of the initialresultant temperature distribution with the associated initial H fieldstrength value (in this case, “initially” means the result of the firstor possibly the third calculation step), the user can dispense with auser input and in this way it is possible to save or otherwise useresources of the computer device on which the inventive simulationmethod is implemented.

Particularly in the case of the calculation of the H field strengthvalue that is to be set in the first calculation step (T selection),variants of the inventive simulation method do not perform numerousiterations on the basis of the “trial and error” principle which involveresultant temperature distributions from chosen field strength valuesbeing calculated by means of a numerical solution to the BHTE so as toiteratively arrive at the sought H field strength value. Such iterationsin which a BHTE is numerically solved each time require very manyresources in terms of computation power, computation time and memoryrequirement and are therefore not suitable for a simulation tool whichis intended to provide a user with an overview of planned therapyoptions. Instead, the BHTE is numerically solved just exactly twice inthe first calculation step, as explained further below.

In one embodiment of the inventive simulation method, the H fieldstrength value is calculated in the first calculation step (T selection)on the basis of a prescribed characteristic curve—for example derivedfrom a reference measurement—which indicates a relationship between(reference) power absorption rate and H field strength.

In one specific variant of this embodiment, the first calculation stephas the following steps: calculation of an average power absorptiondensity (or of an equivalent variable) in the applicator magnetic fieldin the deposit volume, wherein the relative power absorption density (oran equivalent variable) is calculated on the basis of a measuredgeometric distribution of the nanoparticles, a BHTE describing the modelis numerically solved precisely once in order to obtain a basaltemperature distribution without power absorption, and a BHTE describingthe model is numerically solved precisely once in order to obtain arelative temperature increment distribution on the basis of the relativepower absorption density; and wherein the relative power absorptiondensity is scaled by a temperature-based scaling factor which isobtained on the basis of the at least one prescribed temperature limitvalue, the basal temperature distribution and the relative temperatureincrement distribution; calculation, on the basis of the calculatedaverage power absorption density and the calculated mass of thenanoparticles, of a reference power absorption rate which indicates thespecific power absorption rate of an undiluted magnetic fluid containingthe nanoparticles, for example, usually based on the mass of thenanoparticles in W/g; and calculation of an H field strength value onthe basis of the calculated reference power absorption rate and aprescribed characteristic curve—for example derived from a referencemeasurement—which relates to a relationship between reference powerabsorption rate and applied H field strength; furthermore, as an option,calculation of the resultant temperature distribution on the basis ofthe basal temperature distribution, the relative temperature incrementdistribution and the temperature-based scaling factor.

The basal temperature distribution calculated in the first calculationstep and/or the relative temperature increment distribution can beprovided for at least one further use beyond the first calculation step,for example can be stored in the main memory and/or on a hard disk. Byway of example, the use may relate to the fast calculation, in thesecond calculation step (for the “fast H controller” embodiment), of aresultant temperature distribution on the basis of an H field strengthvalue that has been input by the user, without the BHTE needing to benumerically solved again in order to obtain the temperature distributionand/or the relative temperature increment distribution.

In the second calculation step in the “fast H controller” embodiment,regardless of the number of prescribed and/or user-defined H fieldstrength values, a provided (previously calculated in the firstcalculation step) basal temperature distribution and/or provided(previously calculated in the first calculation step) relativetemperature increment distribution can be used, for example theaforementioned temperature distributions (basal and relativeincremental) which are provided from the first calculation step. Hence,it is possible to prevent the BHTE from being numerically solvedagain—which is intensive in terms of memory and computation time—for thecalculation of the basal and/or relative incremental temperaturedistribution.

In particular embodiments of the inventive simulation method (“Hcontroller” embodiment), the second calculation step is designed suchthat the basal temperature distribution and the relative incrementaltemperature distribution are not used from the main memory or from thehard disk but rather need to be recalculated as part of the secondcalculation step by numerically solving the BHTE. In this case,regardless of the number of prescribed and/or user-defined H fieldstrength values, the BHTE is numerically solved no more than twice,namely once to calculate the basal temperature distribution and once tocalculate the relative temperature increment distribution.

In variants of the inventive simulation method, in the secondcalculation step (H controller, fast H controller) the resultanttemperature distribution that is to be expected is calculated by meansof power-absorption-based scaling (“K”) of a calculated or providedrelative temperature increment distribution. Specific embodiments of thesimulation method comprise the following steps in the second calculationstep (H controller, fast H controller): calculation of a relative powerabsorption density distribution (or of an equivalent variable) and arelative average power absorption density (or an equivalent variable) onthe basis of a measured geometric distribution of the nanoparticles;provision of a basal temperature distribution on the basis of anumerical solution to a BHTE describing the model without powerabsorption, and provision of a relative temperature incrementdistribution on the basis of a numerical solution to a BHTE describingthe model with the calculated relative power absorption densitydistribution [the provision is made by reading in the previously storeddistributions (for the “fast H controller”) or by numerically solvingthe BHTE again (for the “H controller”)]; performance of the followingsteps for each H field strength value from the plurality of prescribed Hfield strength values and/or the user-defined H field strength value:calculation of a reference power absorption rate which indicates thespecific power absorption rate of an undiluted magnetic fluid containingthe nanoparticles, for example, wherein the calculation is based on therespective H field strength value and a prescribed characteristiccurve—for example derived from a reference measurement—which relates toa relationship between reference power absorption rate and applied Hfield strength; calculation, on the basis of the reference powerabsorption rate and the calculated mass of the nanoparticles in thedeposit volume, of an average power absorption density;power-absorption-based scaling, i.e. calculation of apower-absorption-based scaling factor on the basis of the respectiveaverage power absorption density and the relative power absorptiondensity; calculation of a respective resultant temperature distributionon the basis of the basal temperature distribution, the relativetemperature increment distribution and the power-absorption-basedscaling factor.

According to the invention, a further computer-aided simulation method(T selection) for providing assistance in thermotherapy planning isproposed. The thermotherapy comprises hyperthermia treatment of a tumorvolume in a body volume of a human body. The hyperthermia treatmentcomprises the application of a magnetic field in a treatment volume bymeans of a magnetic field applicator. Thermal energy can be introducedinto at least one deposit volume by power absorption in the appliedmagnetic field by means of magnetic, paramagnetic and/orsuperparamagnetic nanoparticles deposited in the body. The simulationmethod relates to the calculation of an H field strength that needs tobe set on the applicator on the basis of a geometric distribution of thenanoparticles and at least one prescribed temperature limit value whichis not meant to be exceeded by the hyperthermia treatment (T selection).The H field strength value is calculated on the basis of a prescribedcharacteristic curve—for example derived from a referencemeasurement—which indicates the relationship between power absorptionrate and H field strength.

In one particular embodiment, the simulation method has the followingsteps: calculation of an average power absorption density (or of anequivalent variable) in the applicator magnetic field in the depositvolume, wherein a relative power absorption density is calculated on thebasis of a measured geometric distribution of the nanoparticles, a BHTEdescribing the model is numerically solved precisely once in order toobtain a basal temperature distribution without power absorption, and aBHTE describing the model which is numerically solved precisely once inorder to obtain a relative temperature increment distribution on thebasis of the relative power absorption density; and wherein the relativepower absorption density is scaled by a temperature-based scaling factorwhich is obtained on the basis of the at least one prescribedtemperature limit value, the basal temperature distribution and therelative temperature increment distribution; calculation, on the basisof the calculated average power absorption density and the calculatedmass of the nanoparticles, of a reference power absorption rate whichindicates the specific power absorption rate of an undiluted magneticfluid containing the nano-particles, for example; calculation of an Hfield strength value on the basis of the calculated reference powerabsorption rate and a prescribed characteristic curve—for examplederived from a reference measurement—which relates to a relationshipbetween reference power absorption rate and applied H field strength;preferably, calculation of the resultant temperature distribution on thebasis of the basal temperature distribution, the relative temperatureincrement distribution and the temperature-based scaling factor; andprovision of the calculated H field strength value and optionally theresultant temperature distribution associated with said H field strengthvalue, calculated as described above, in order to provide assistance forthe user in planning the thermotherapy.

The invention proposes yet a further computer-aided simulation method (Hcontroller; fast H controller) for providing assistance in thermotherapyplanning. The thermotherapy comprises hyperthermia treatment of a tumorvolume in the body volume of a human body. The hyperthermia treatmentcomprises the application of a magnetic field in a treatment volume bymeans of a magnetic field applicator. Thermal energy can be introducedinto at least one deposit volume by power absorption in the appliedmagnetic field by means of magnetic, paramagnetic and/orsuperparamagnetic nanoparticles deposited in the body. The simulationmethod relates to the calculation, for each H field strength value froma plurality of prescribed H field strength values and/or a user-definedH field strength value, of a temperature distribution that is to beexpected for at least some of the body volume (H controller). Thetemperature distribution that is to be expected is calculated by meansof power-absorption-based scaling of a calculated or provided relativetemperature increment distribution.

In particular variants, regardless of the number of prescribed and/oruser-defined H field strength values, e.g. in order to avoid numericallysolving the BHTE again, a previously calculated (in the T selectionstep) basal temperature distribution and/or a previously calculated (inthe T selection step) relative temperature increment distribution is/areused (fast H controller). In addition or as an alternative, it ispossible—regardless of the number of prescribed and/or user-defined Hfield strength values—for a BHTE describing the model to be numericallysolved never (fast H controller), once (H selection) or no more thantwice (H controller) in order to subsequently determine the resultanttemperature distribution. In the “never” case (fast H controller), theresultant temperature distribution is compiled from the basaltemperature distribution—previously calculated (earlier calculationsteps) by means of BHTE—and the previously calculated relativetemperature increment distribution by means of power-absorption-basedscaling. In the “once” case (H selection), the resultant temperaturedistribution is numerically calculated directly, i.e. without splittinginto basal and incremental components, by solving the BHTE. In the“twice” case (H controller), the basal and incremental temperaturedistributions are numerically calculated in this (second) calculationstep individually by solving the BHTE and are then compiled to producethe resultant temperature distribution by means ofpower-absorption-based scaling.

In particular embodiments, the simulation method has the followingsteps: calculation of a relative power absorption density distribution(or of an equivalent variable) and a relative average power absorptiondensity (or an equivalent variable) on the basis of a measured geometricdistribution of the nanoparticles; provision of a basal temperaturedistribution on the basis of a numerical solution to a BHTE describingthe model without power absorption, and provision of a relativetemperature increment distribution on the basis of a numerical solutionto a BHTE describing the model with the calculated relative powerabsorption density distribution; performance of the following steps foreach H field strength value from the plurality of prescribed H fieldstrength values and/or the user-defined H field strength value:calculation of a reference power absorption rate which indicates thespecific power absorption rate of an undiluted magnetic fluid containingthe nanoparticles, for example, wherein the calculation is based on therespective H field strength value and a prescribed characteristiccurve—for example derived from a reference measurement—which relates toa relationship between reference power absorption rate and applied Hfield strength; calculation, on the basis of the reference powerabsorption rate and the calculated mass of the nanoparticles in thedeposit volume, of an average power absorption density;power-absorption-based scaling, i.e. calculation of apower-absorption-based scaling factor on the basis of the respectiveaverage power absorption density and the relative power absorptiondensity; calculation of the respective resultant temperaturedistribution on the basis of the basal temperature distribution, therelative temperature increment distribution and thepower-absorption-based scaling factor; provision of the calculatedtemperature distributions in order to provide assistance for the user inplanning the thermotherapy.

The invention also proposes a computer program which prompts theexecution of one of the simulation methods described herein when thecomputer program is executed on a programmable computer device, forexample a computer in a clinic or doctor's practice. The computerprogram may be stored or present in stored form on a machine-readabledata storage medium, for example a permanent or rewritable medium in orassociated with a programmable computer device or a CD-ROM, DVD or a USBstick. In addition or as an alternative, the computer program can beprovided for download onto a programmable computer device, for examplevia a data network such as the Internet or a communication link such asa telephone line and/or a wireless connection.

Furthermore, the invention provides a computer device which is designedto provide assistance in thermotherapy planning. In this case, thethermotherapy comprises hyperthermia treatment of a tumor volume in thebody volume of a human body, wherein the hyperthermia treatmentcomprises the application of a magnetic field in the treatment volume bymeans of a magnetic field applicator. In this case, thermal energy canbe introduced into at least one deposit volume by power absorption inthe applied magnetic field by means of magnetic, paramagnetic and/orsuperparamagnetic nanoparticles deposited in the body. The computerdevice has the following components: a first calculation component whichis designed to calculate an H field strength value that needs to be seton the applicator on the basis of a geometric distribution of thenanoparticles and at least one prescribed temperature limit value whichis not meant to be exceeded by the hyperthermia treatment; a secondcalculation component which is designed to optionally calculate atemperature distribution that is to be expected for at least some of thebody volume for each H field strength value from a plurality ofprescribed H field strength values, and/or a user-defined H fieldstrength value; an optional third component designed to calculate atemperature distribution that is to be expected for a maximum admissibleH field strength value when the H field strength value calculated in thefirst calculation step is greater than this maximum admissible H fieldstrength value; and a component for providing (the user with) thecalculated H field strength value that needs to be set on the applicatorand optionally at least one calculated temperature distribution (e.g.associated with the aforementioned H field strength value) in order toprovide assistance for the user in planning the thermotherapy.

A further inventive computer device (“T selection”) is designed toprovide assistance in thermotherapy planning, wherein the thermotherapycomprises hyperthermia treatment of a tumor volume in a body volume of ahuman body and wherein the hyperthermia treatment comprises theapplication of a magnetic field in a treatment volume by means of amagnetic field applicator. In this case, thermal energy can beintroduced into at least one deposit volume by power absorption in theapplied magnetic field by means of magnetic, paramagnetic and/orsuperparamagnetic nanoparticles deposited in the body. The computerdevice has a component which is designed to calculate an H fieldstrength that needs to be set on the applicator on the basis of ageometric distribution of the nanoparticles and at least one prescribedtemperature limit value which is not meant to be exceeded by thehyperthermia treatment. In this case, the component has a module forcalculating the H field strength value on the basis of a prescribedcharacteristic curve—for example derived from a referencemeasurement—wherein the characteristic curve indicates a relationshipbetween power absorption rate and H field strength. In one preferredinventive variant of the computer device, the computer device has thefollowing modules: a module for calculating an average power absorptiondensity in the applicator magnetic field in the deposit volume, whereina relative power absorption density is calculated on the basis of ameasured geometric distribution of the nanoparticles, a BHTE describingthe model is numerically solved precisely once in order to obtain abasal temperature distribution without power absorption, and a BHTEdescribing the model is numerically solved precisely once in order toobtain a relative temperature increment distribution on the basis of therelative power absorption density; and wherein the relative powerabsorption density is scaled by a temperature-based scaling factor whichis obtained on the basis of the at least one prescribed temperaturelimit value, the basal temperature distribution and the relativetemperature increment distribution; a module for calculating, on thebasis of the calculated average power absorption density and thecalculated mass of the nanoparticles, a reference power absorption ratewhich indicates the specific power absorption rate of an undilutedmagnetic fluid containing the nanoparticles, for example; a module forcalculating an H field strength value on the basis of the calculatedreference power absorption rate and a prescribed characteristiccurve—for example derived from a reference measurement—which relates toa relationship between reference power absorption rate and applied fieldstrength; optionally, a module for calculating the resultant temperaturedistribution on the basis of the basal temperature distribution, therelative temperature increment distribution and the temperature-basedscaling factor; a module for providing the calculated field strengthvalue in order to provide assistance for the user in planning thethermotherapy; and optionally a module for providing the resultanttemperature distribution associated with the calculated H field strengthvalue in order to provide assistance for the user in planning thethermotherapy.

A further inventive computer device (“H controller”, “fast Hcontroller”) is designed to provide assistance in thermotherapyplanning, wherein the thermotherapy comprises hyperthermia treatment ofa tumor volume in a body volume of a human body and wherein thehyperthermia treatment comprises the application of a magnetic field ina treatment volume by means of a magnetic field applicator. In thiscase, thermal energy can be introduced into at least one deposit volumeby power absorption in the applied magnetic field by means of magnetic,paramagnetic and/or superparamagnetic nanoparticles deposited in thebody. The computer device has a component which is designed tocalculate, for each field strength value from a plurality of prescribedfield strength values and/or a user-defined field strength value, atemperature distribution that is to be expected for at least some of thebody volume. In this case, the component has a module for calculatingthe temperature distribution that is to be expected by means ofpower-absorption-based scaling of a calculated or provided temperatureincrement distribution. Preferably, the component is designed tocalculate the temperature distribution that is to be expected in orderto use, regardless of the number of prescribed and/or user-defined Hfield strength values, a provided (previously calculated in the Tselection step) basal temperature distribution and/or a provided(previously calculated in the T selection step) relative temperatureincrement distribution (fast H controller).

With further preference, the component for calculating the temperaturedistribution that is to be expected is designed to calculate, regardlessof the number of prescribed and/or user-defined field strength values,no more than two temperature distributions (H controller), namely abasal temperature distribution and/or a relative temperature incrementdistribution.

With further preference, the inventive computer device has the followingmodules (“H controller”, “fast H controller”): a module for calculatinga relative power absorption density distribution and a relative averagepower absorption density on the basis of a measured geometricdistribution of the nanoparticles; a module for providing a basaltemperature distribution on the basis of a numerical solution to a BHTEdescribing the model without power absorption, and providing a relativetemperature increment distribution on the basis of a numerical solutionto a BHTE describing the model with the calculated relative powerabsorption density distribution; a module for performing the followingsteps for each H field strength value from the plurality of prescribed Hfield strength values: calculation of a reference power absorption ratewhich indicates the specific power absorption rate of an undilutedmagnetic fluid containing the nanoparticles, for example, wherein thecalculation is based on the respective H field strength value and aprescribed characteristic curve—for example derived from a referencemeasurement—which relates to a relationship between reference powerabsorption rate and applied H field strength; calculation, on the basisof the reference power absorption rate and the calculated mass of thenanoparticles in the deposit volume, of an average power absorptiondensity; power-absorption-dependent scaling, i.e. calculation of apower-absorption-based scaling factor on the basis of the respectiveaverage power absorption density and the relative power absorptiondensity; calculation of a respective resultant temperature distributionon the basis of the basal temperature distribution, the relativetemperature increment distribution and the power-absorption-basedscaling factor; and a module for providing the calculated temperaturedistributions in order to provide assistance for the user in planningthe thermotherapy.

Furthermore, the invention proposes a system which comprises a computerdevice as outlined above and a magnetic field applicator or the partsthereof. The inventive computer device may be designed for use with themagnetic field applicator, for example by virtue of the use of, by wayof example, an H field strength that can be set on the applicator as amaximum and/or metrological input data, such as a characteristic curvederived from a reference measurement, for example, for the powerabsorption on the basis of the H field strength. The magnetic fieldapplicator can be matched to the inventive computer device by virtue ofthe applicator accepting data from said applicator, such as an H fieldstrength value that needs to be set (for example after the end of thesimulations as a default value and/or by the instigation of the user).For this embodiment, the computer device can also be used for planningor control.

The invention also proposes a system which comprises a computer programas outlined above, a data storage medium as outlined above, a computerdevice as outlined above or a system as outlined above, and which alsocomprises a magnetic fluid which contains magnetic nanoparticles. Theinventive computer program may match the use of the magnetic fluid byvirtue of the power absorption v. H field strength for this specificcharacteristic curve being used for the simulations. The computerprogram may be designed for the use of a plurality of magnetic fluids;in this case, the user would need to input the fluid that is actually tobe used before the start of the simulations, for example by selectingfrom a menu in a GUI of the computer device.

Instead of in a magnetic fluid, the nanoparticles could, in principle,also be instilled into the body of the patient in another form ofpresentation. All forms of presentation are intended to be covered bythe invention.

A further subject of the invention comprises a method for controlledheating of an organ or tissue, having the following steps: introductionof magnetic, paramagnetic and/or superparamagnetic nanoparticles into anorgan volume or tissue volume; ascertainment of the nanoparticlequantity and/or distribution in the organ volume or tissue volume;calculation of an H field strength that needs to be set on the basis ofone of the appropriate methods outlined above or of a resultanttemperature distribution on the basis of one of the appropriate methodsoutlined above; and deposition of thermal energy by means of applicationof a magnetic field, wherein the applied H field strength is set, whichcorresponds to the calculated field strength or to the field strengthderived from a calculated temperature distribution, in each case with adeviation of +/−10%, preferably +/−5%, particularly +/−1%. This methodcan be performed in vitro if appropriate.

A method for treating a tumor in a patient contains the following steps:introduction of a suitable quantity of magnetic, paramagnetic and/orsuperparamagnetic nanoparticles into a tumor volume; ascertainment ofthe nanoparticle quantity and/or distribution in the tumor volume;calculation of a field strength that needs to be set on the basis of oneof the appropriate methods outlined above or of a resultant temperaturedistribution on the basis of one of the appropriate methods outlinedabove; deposition of thermal energy by means of application of amagnetic field, wherein the applied field strength is set, whichcorresponds to the calculated field strength or the field strengthderived from a calculated temperature distribution, in each case with adeviation of +/−10%, preferably +/−5%, particularly +/−1%.

Suitable methods for treating tumors or for controlled heating of anorgan or tissue by means of suitable nanoparticles and application of amagnetic field that are able to be improved by the inventive simulationmethods are known from the prior art. For example, Maier-Hauff et al.(2010, supra) describe a successful study on 66 patients with braintumors, 59 of them with glioblastoma. Furthermore, similar methods weresuccessfully performed on prostate carcinoma patients (Johannsen, M., etal. (2007), Eur Urol. 52(6): 1653-61. Epub 2006 Nov. 17; Johannsen, M.,et al. (2010), Int J Hyperthermia 26(8): 790-5.) and also in a study inwhich patients with various tumors, namely chondrosarcoma, rectalcarcinoma, cervical carcinoma, rhabdomyosarcoma, parapharyngeal sarcomaand prostate carcinoma, were included and treated (Wust, P., et al.(2006), Int J. Hyperthermia. 22(8): 673-85.). Clinical data fromsuitable methods are summarized in Thiesen, B. and A. Jordan (2008, IntJ. Hyperthermia. 24(6): 467-74). Further methods for the treatment oftumors or for the controlled heating of an organ or tissue by means ofsuitable nanoparticles and application of a magnetic field are knownfrom US 20080268061, US 20110052609, WO 2009/100716 and WO 2011/082796.Solid tumors are preferred, particularly local and locally advancedtumors, or systemic tumor diseases, which cause local problems such asinoperable metastases. Examples are brain tumors, e.g. glioblastoma andastrocytoma, brain metastases, prostate cancer, pancreatic cancer,hepatocellular carcinoma, throat and neck tumors, cancer of the bladder,stomach cancer, intestinal cancer, renal cell carcinoma, ovariancarcinoma, cervical carcinoma, sarcomas, basal cell carcinoma andmelanoma.

The inventive methods for the controlled heating of an organ or tissuecan be used for the treatment of arthrosis, arthritis and otherrheumatic joint diseases, for example. The treatment of these diseasesby means of similar methods is known from WO 01/13949, for example.

Advantages of the Invention

The invention provides the user with extensive opportunities forobtaining an overview of various therapy options and the effects thereofand planning the therapy accordingly. To this end, the inventivesimulation tool automatically provides a temperature distribution whichhas been calculated on the basis of a maximum field strength, but takingaccount of prescribed/input temperature limit values. The inventionallows two (or more) limit values to be taken into account, e.g. amaximum temperature outside the treatment area, in order to look afterthe healthy tissue, and/or maximum temperature inside the treatmentarea, in order to destroy the tumor but nevertheless to limit an inputof power into the body of the patient. In principle, it is conceivableto prescribe a geometric temperature limit value distribution whichtakes account of special features specific to tissue, for example.

The inventive simulation tool may be “intelligent” in the sense that itrejects the resultant field strength value if it is greater than amaximum settable value on the applicator/for a given patient, forexample. In this case, the simulation tool calculates a new temperaturedistribution afresh on the basis of the maximum settable field strengthvalue.

The user can decide whether he wishes to use the field strength valueresulting therefrom without performing further simulations. As a result,the inventive simulation tool provides the experienced user with theopportunity to terminate the planning without wasting resources. It isthen already possible to use the simulation tool for the next patient,for example.

The user can also decide to obtain a more extensive overview. In thiscase, the simulation tool can calculate temperature distributions for aplurality of field strength values, for example. These calculations canbe performed in the background as soon as the user has made anappropriate input (if applicable, the simulation tool can also beconfigured such that it automatically starts these calculations in thebackground after the initial temperature distribution has beenprovided). It is therefore already possible for the user to work withthe results from the first calculation step without delay.

These temperature distributions do not take account of the temperaturelimit values discussed above. Hence, the user is in this case providedwith an extensive decision basis by means of the effects of particularfield strength settings and, depending on the aim of the therapy, theseverity of the disease, the tissue that is possibly affected, etc., mayunder some circumstances decide upon a therapy option in which thetemperature limit value or the limit values are not observed everywhere.

In addition or as an alternative, the simulation tool accepts a fieldstrength value that is input by the user and calculates the resultanttemperature distribution for said field strength value. In particularembodiments of the invention, this is accomplished in time-savingfashion without numerically solving the BHTE again, which means that theresult is available to the user essentially without any waiting time,for example after just one second or less.

By means of the inventive simulation tool, the aim of therapy andpossible side effects can thus be predicted better and more easily andcan thus also be controlled better and more extensively.

In this case, the inventive simulation tool requires—both forcalculating the temperature distribution using prescribed temperaturelimit values (first calculation step, see below temperature selection or“T selection”) and for calculating a scale of temperature distributionsor a user-defined field strength value (second calculation step, seebelow H field strength controller, “H controller” or “fast Hcontroller”)—precisely two numerical solutions to a BHTE with subsequentscaling, that is to say manages without iterative trial and errorapproximations (by means of recurrently restarted numerical solutions tothe BHTE) to the sought field strength value in the first step andcalculate the relevant temperature distributions by means of simplescaling for an arbitrary number of field strength values, in principle,in the second step. As a result, the simulation tool is user friendlybecause the calculation results are available extremely precisely andnevertheless quickly. The inventive simulation tool requires only afraction of the CPU computation time in comparison with an iterativetrial and error approach and therefore makes a clinically feasibleapplication possible in the first place, even using computers which areolder/have limited resources.

BRIEF DESCRIPTION OF THE FIGURES

Further aspects and advantages of the invention will now be described inmore detail with reference to the appended figures, in which:

FIG. 1 shows a flowchart for a general program cycle from a firstexemplary embodiment of an inventive simulation tool having thefollowing main steps: “patient data”, “image fusion”, “segmentation”,“temperature simulation” and “therapy plan”. In the main step,“temperature simulation”, the “temperature simulator” program package(called “simulator” for short) is called. In the first exemplaryembodiment (FIG. 1 to FIG. 5), the simulator package contains threeautarkic simulator (execution) programs (.exe), but the simulator canalso be executed as a linkable program library having three mainsubroutines (“mainsubroutines”) (see second exemplary embodiment FIG. 6to FIG. 9);

FIG. 2 shows a flowchart for a program cycle from a first exemplaryembodiment of an inventive simulator, having a stipulated sequence ofcalls to the simulator programs;

FIG. 3 shows a flowchart for a program cycle from the simulator fromFIG. 2 in what is known as mode 2 (execution program “sim_h.exe”:selection of the H field strength, “H selection” for short;

FIG. 4 shows a flowchart for a program cycle from the simulator fromFIG. 2 in mode 1 (execution program “sim_t.exe”: temperature selection,“T selection” for short);

FIG. 5 shows a flowchart for a program cycle from the simulator fromFIG. 2 in mode 3 (execution program “sim_hr.exe”: what is known as an Hfield strength controller, “H controller” for short);

FIG. 6 shows a flowchart for a program cycle from a second exemplaryembodiment of an inventive simulator, having a stipulated sequence ofcalls to the simulator main subroutines;

FIG. 7 shows a flowchart for a program cycle from one of the three mainsubroutines of the simulator from FIG. 6 in mode 2, namely“mainsubroutine_sim_h_voxel_win” (H selection);

FIG. 8 shows a flowchart for a program cycle from one of the three mainsubroutines of the simulator from FIG. 6 in mode 1, namely“mainsubroutine_sim_t_voxel_win” (T selection); and

FIG. 9 shows a flowchart for a program cycle from one of the three mainsubroutines of the simulator from FIG. 6 in mode 3, namely“mainsubroutine_sim_hr_voxel_win” (what is known as a “fast Hcontroller”).

EXEMPLARY EMBODIMENTS OF THE INVENTION

The text below describes a first exemplary embodiment of an inventivesimulation tool in more detail. FIG. 1 shows a schematic representationof a program cycle from an inventive simulation method in the form of apiece of simulation software which has been developed with the aim ofproviding assistance in NanoTherm® therapy.

The software provides the treating doctor (neurosurgeon, radiologist)with orientation assistance during thermotherapy for malignant tumors,such as brain tumors, in order to estimate the treatment temperaturesand the magnetic field strength required therefor on the basis of a BHTEthat describes the model.

In order to estimate the therapy field strength, the software providesthe opportunity to import and register image data in DICOM format, e.g.from MRI and CT scans. It is likewise possible to perform contouringoperations on all image files used (segmentation).

The software takes the user to the result in steps by querying therequired parameters. This involves firstly 3D visualization, whichreveals the contoured areas such as a tumor, a catheter and nanoparticledeposits. Secondly, an estimate of the temperature distribution in thedenoted areas on the basis of field strength and therapy time is shown.This is particularly important for areas which can be treated only to arestricted degree, such as in the head on a frontobasal basis in thearea of the optic chiasma (hypothalamus), the sylvian fissure, in whichthe vascular tree of the middle cerebral artery runs, the corpuscallosum or the brain stem.

The data that are input and the simulations that are produced can bestored and printed, and various scenes can be produced per patient. Thesimulation tool thus provides assistance for the doctor or user inpreparing the therapy plan. By way of example, the therapy plan isreleased by a signature from the doctor who is responsible on theprinted therapy proposal. The simulation results presented in thetherapy proposal can be used for orientation purposes and do not need tomake any particular demands on accuracy.

The simulation software can be operated on a customer's own hardware,and appropriate minimum requirements need to be met. The ambientconditions correspond to those of an environment for the application ofmedical software.

Overview of the First Exemplary Embodiment

As a program package, the software comprises a “temperature simulator”(subsequently also called “simulator” for short). Following theintroduction of magnetic fluid into a tumor, the simulator allowssimulation of the temperature distribution in the body area on the basisof the magnetic field strength of the therapy appliance (applicator).The simulator calculates a particular field strength as part of anonbinding recommendation. To perform the therapy, it is additionallypossible to take temperature measurements during the therapy, as aresult of which simulation results and temperature measurements togethercan form the basis for assessment of the therapy by the doctor, saidtemperature measurements preferably being influential. Naturally, thesimulation results are neither a prerequisite for therapy being able tobe performed nor binding for performance.

FIG. 1 shows the main steps of a program cycle. The “simulator” programpackage is called in a main step “temperature simulation”. The followingmain tasks are performed by the simulator:

-   -   Simulation of the three-dimensional temperature distribution        which probably results from the application of the magnetic        field to superparamagnetic or ferrimagnetic nanoparticles, for        example, on the assumption of a simplified physical model (this        is described more precisely below); and    -   estimation of the H field strength, for example on the basis of        particular temperature selections for the patient model.

The simulator package is not directly part of the core of the simulationsoftware, but rather is linked to the core by means of a firmly definedexternal I/O interface as part of external SOUP (“Software of UnknownProvenance”).

The simulator package comprises three independent simulator programssim_t, sim_h and sim_hr, which are described in FORTRAN77 (executionprograms “sim_t.exe”, “sim_h.exe” and “sim_hr.exe”). The order of thecalls to the simulator programs and the management of the simulator dataare undertaken by the core of the simulation software, in this casecalled the “main program core”. The main program core also manages allother main steps which have been shown in FIG. 1, such as image fusion,segmentation, inter alia. In this first exemplary embodiment, the datainterchange between the core and the simulator, including the output ofprogram termination messages, is effected by reading/writing from/to ahard disk directory.

The “Temperature Simulation” Program Main Step in Detail

The time sequence of the calls to the simulator programs in the“temperature simulation” main step is controlled or managed from themain program core and can take place on the basis of a scheme as shownin FIG. 2.

It is stipulated that after a respective change of user in the GUI(“Graphical User Interface”) upon a change from the segmentation menu tothe temperature simulation menu, the T selection program sim_t.exestarts first in this menu (mode=1). This program automatically (i.e.without any input by the user) has the following two limit temperatureselections to comply with:

-   -   43° C. maximum outside the PTV (“non-PTV 43° C. limit”)    -   80° C. maximum everywhere else, i.e. de facto inside the PTV        (“whole-body 80° C. limit”).

In this case, PTV (“planning target volume”) denotes the treatmentarea/treatment volume, i.e. the volume that is to be treated or to beheated. This can be stipulated by the user in the segmentation main step(the simulation software can, for example on the basis of manuallyperformed tumor contouring, make a proposal for the treatment volume,for example tumor volume plus border, which can be accepted or alteredby the user, that is to say can be restricted or expanded, for example).A temperature value of 43° C. is implemented as a threshold temperature,above which damage to the healthy tissue can increasingly occur, whichneeds to be avoided as far as possible. A temperature value of 80° C. isimplemented as a general temperature limit which must not be exceededthroughout the body, that is to say not even in the tumor volume, forexample; this simultaneously limits the power absorption by the body asa whole. Of the two limit conditions, the one which occurs at the lowerH field strength becomes effective.

However, it should be noted that the limit values of 43° C. and 80° C.are applied to the treatment of tumors in a body volume such as thehuman head, for example. Hyperthermia treatments relating to other bodyvolumes, such as the prostate area, may be oriented to the observance ofother limit values. By way of example, the limit value in the PTV may be100° C. instead of 80° C. for thermotherapy on a prostate tumor.

Following the execution of sim_t.exe, the simulation software first ofall examines internally, i.e. without the user being notified of thevalues by GUI, what the level of the H field strength value that isoutput internally as a sim_t output is. If this value is greater than 15kA/m (kiloamps per meter), a pass of sim_h.exe (mode 2) startsautomatically (without the user needing to make any input) with theselection 15 kA/m (H selection), where 15 kA/m is the maximum H fieldstrength value that can be set on the magnetic field applicator.

If the sim_t.exe output value is less than or equal to 15 kA/m, it isnot necessary for a sim_h.exe program run to be automatically started,and the next step follows directly.

In this next step, the temperature distribution and the following valuesare output graphically to the GUI:

-   -   the H field strength recommendation (the output from sim_t.exe        or 15 kA/m from the automatic sim_h.exe run); this output is        provided in the GUI window; and    -   the maximum temperature reached outside the PTV, and also the        maximum temperature reached in the treatment area, and also        possibly further output variables; this output is provided in a        popup window.

There may be cases in which the prescribed temperature of 43° C., forexample, outside the PTV is not reached, specifically when the limitcondition of 80° C. in the PTV has already been reached at a lower Hfield strength value.

On the basis of the output of the initial result as described above, theuser can prompt the “H controller” or field strength controllersim_hr.exe (mode=3) to be started (in other exemplary embodiments, thefield strength controller can also be started automatically). The systemperforms these calculations in the background, so that the user has theopportunity to look at the initially ascertained temperaturedistribution in the meantime. Optionally, the user can skip the start ofthe H controller, or can terminate the calculation in due fashion.

In the configuration example described here, the H controller calculatesten temperature distributions for the following fixed values of the Hfield strength (multiple H selection): 5 kA/m, 6 kA/m, 7 kA/m, 8 kA/m, 9kA/m, 10 kA/m, 11 kA/m, 12 kA/m, 13 kA/m and 14 kA/m. This covers theentire range of settings for the applicator used in this example insteps of 1 kA/m. When the pass has taken place, the user is providedwith the opportunity to look at the temperature distributions for theseH field strength values by means of the setting of a position on therelevant bar.

The indicated list of H field strength values relates to the treatmentof glioblastomas in the head, for example. Other values can beprescribed for treatments in other body areas or volumes. By way ofexample, for the treatment of prostate tumors, a step size of just 0.5kA/m can be prescribed for values between 2 kA/m, 2.5 kA/m, . . . , 8kA/m.

The H controller has no kind of limits for the temperatures reached(other than sim_t.exe in the initial run). Therefore, there may be casesin which the temperatures reached are higher (or lower) than 80° C. inthe calculation area and/or higher (and/or lower) than 43° C. outsidePTV.

If the user wishes to obtain yet a further review besides the fieldstrength recommendation of sim_t.exe and possibly the ten results forthe H controller, he has the option of manually typing the desired inputH field strength value in a GUI window. The calculation then starts inthe H selection mode (sim_h.exe). He can repeat this as desired.

In the H selection mode, there are no kind of limits in respect of thetemperatures reached (other than sim_t.exe in the initial run).Therefore, there may be cases in which the temperatures reached arehigher (or lower) than 80° C. in the calculation or treatment areaand/or higher (and/or lower) than 43° C. outside the PTV.

Furthermore, the user can return to the segmentation step at any time inorder to make corrections on the PTV or other segmentation corrections.In this case, the simulator changes to the initial state. As soon as anew segmented data record (what is known as a “labeled volume” or LVdata record, see below) is available and the user changes from thesegmentation step to the temperature calculation menu, the wholeprocedure is repeated, i.e. the simulator starts with the sim_t.exeinitial run.

General Technical Approach

The production of the temperature rise by nanoparticles in the magneticfield is notionally split into two steps:

1. In a first step, the nanoparticles in the magnetic field prompt alocal power absorption, indicated by a power absorption density (W/m³)or rate (W/kg), in this case denoted generally by SAR (“SpecificAbsorption Rate”);

2. In a second step, this SAR acts as a (main) source of the temperaturerise.

The simulation of temperatures which are obtained from values of the Hfield strength is split into two main steps in the simulator:

1. Ascertainment of an SAR distribution for a given H field strength bywhat is denoted here as an “SAR solver” (i.e. an appropriate calculationcomponent or a calculation module); and

2. Ascertainment of the temperature distribution (“T distribution”) fromthe SAR distribution (in what is known as the “T solver”).

In the SAR solver introduced above, the SAR is determined frommetrological data, for example on the basis of a CT (computedtomography) data record with marked magnetic fluid deposits (see below)and a metrologically ascertained dependency of the iron core SAR on theH field strength. Physical approximations can be used forimplementation, as are presented in Gneveckow et al. 2004, for example.

In the T solver, a time-dependent BHTE is numerically solved usingfinite differences by taking account of particular circumstances of theapplication with magnetic fluids. The T solver can use explicittemperature gradients, as are described in Nadobny et al. 2007,appendix, for example.

In the simulator package, the SAR solver and the T solver do not formseparate program units, but rather are merged to form a joint program,with the SAR and T solver components being “mixed” in different waysamong one another in the program cycle depending on the selection mode.This is described more precisely below.

The geometric shape of the SAR distribution (and consequently of thetemperature distribution) is highly dependent on the geometric positionsof the nanoparticles or deposits of the magnetic fluid (“nanodeposits”,“deposit volumes”) which are latched in the tissue. These nanoparticlepositions need to be communicated to the simulator as an input in orderto determine the SAR. To this end, a segmented (e.g. binary)three-dimensional data record (in this case denoted as “LV.raw”, LVstanding for “labeled volume”, see below) with marked nanoparticleswhich is produced on the basis of the planning CT needs to be madeavailable to the simulator in the preceding main step “segmentation”.Furthermore, the values of the Hounsfield units at the locations of thenanoparticles need to be known. This is accomplished by reading in thebinary CT file CT.raw.

The power absorption of the nanoparticles in the magnetic field can berepresented in the simulator in different ways. A specific powerabsorption can be quantified by a power absorption rate (“SpecificAbsorption Rate”, SAR) in units of watts per kilogram or gram (W/kg orW/g), the mass which needs to be set in this case being the magneticallyeffective mass, that is to say the mass of the magnetic fluid (in thiscase the SAR is usually indicated in W/kg) or the iron mass in the caseof nanoparticles with an iron core (in this case the SAR is usuallyindicated in W/g), for example. Instead of using a rate, a specificpower absorption can also be indicated via a power absorption density inunits of watts per cubic meter or cubic centimeter (W/m³ or W/cm³). If aspecific power absorption density is involved, the volume would beapproximately the volume of the magnetic fluid.

A specific reference power absorption rate or density can relate to the(measured) power absorption of a magnetic fluid with a carrier (forexample water) and the nanoparticles “dissolved” therein, for example.In this case, the quantity of the nanoparticles in the carrier is knownextremely precisely for such a reference measurement (e.g. in molarmass). The reference statement thus relates to a magnetic fluid in thereference state prior to instillation. The iron core SAR “SAR_fe” usedhere is one such reference statement which is usually based on the ironmass and is indicated in W/g.

The actual power absorption (“Absorbed Power Rate” or “Absorbed PowerDensity”, APD, in W/m³) in the tissue is dependent on the density of themagnetically effective (masses of the) nanoparticles which is present insaid tissue. Hence, the actual or tissue-specific power absorption inthe tissue is altered in comparison with the specific reference powerabsorption in this manner on the basis of density; in general, thenanoparticles in the tissue are present in a lower density than thereference magnetic fluid (they are “diluted”). In order to limit theamount of terms used, the use of APD should be avoided. Even for theactual power absorption, we therefore continue to refer to the SAR or aspatial SAR distribution SAR (x,y,z), for example, even if, strictlyspeaking, the unit W/m³ should be used. The language use is justifiedinsofar as the conversion from SAR to APD or vice versa is a simplematter for the simulator: the actual density of the (iron mass of the)nanoparticles per voxel is known, being derived from the CT data (e.g.grayscale values in HU). The (specific/actual) power absorption rate andpower absorption density can also each be converted into one another bya density factor (magnetically effective mass density in the magneticfluid/in the tissue following instillation). In this sense, we alsorefer to a volume SAR in this case. Hence, the terms “SAR” and “volumeSAR” are equivalent to the term power absorption density, and the issueof the form in which the data are kept by the simulator is one ofnumerical optimization, unlike in the case of the “(reference) powerabsorption rate”, e.g. the SAR_fe, which is based on the mass of thenanoparticles in W/g.

In other words, the term “power absorption density” as used here relatesto a (volume) SAR, while the term “(reference) power absorption rate”relates to a reference variable that is determined metrologicallyoutside the body (in vitro), like SAR_fe.

A volume SAR can thus be understood to mean either a power absorptionrate in W/kg that is deposited in the body or a power absorption densityin units of W/m³ (watts per cubic meter), for example, that is depositedin the body. In addition, a (reference) SAR rate or (reference) powerabsorption rate is also used which is indicated in units of W/kg (wattsper kilogram), W/g (watts per gram), etc., for example.

According to general language use, the supplementary “distribution” isoccasionally omitted. A statement may thus be a location-dependentdistribution, that is to say SAR (x,y,z), or a location-independentvalue, for example an average power absorption density such as thevolume SAR SAR_aver. Whether or not a distribution is present is evidentto a person skilled in the art from the general context.

Re the Functionality of Individual Components or Modules in the FirstExemplary Embodiment

Depending on the input or selection mode, the simulator executionprogram “sim_t.exe”, “sim_h.exe” or “sim_hr.exe” is called from the mainprogram of the simulation software in a particular order in time.Possible orders have already been discussed above.

The simulator provides two ways or options for ascertaining (simulating)an absolute temperature distribution:

-   -   Using the “H selection” (“H” symbolizes H field strength). The        absolute value of the field strength H is prescribed (typically        in kA/m). The temperature distribution T(x,y,z) (in ° C.) is        sought.    -   Using the “T selection” (“T” stands for temperature).        Temperature limit values T_limit (in ° C.) are prescribed, and        the field strength value H (in kA/m) and the associated        temperature distribution T(x,y,z) (in ° C.) are sought.

The T selection is implemented using the execution program “sim_t.exe”,and the H selection is implemented using the execution programsim_h.exe. The third program module, sim_hr.exe, also uses a (multiple)H selection. sim_t.exe and sim_h.exe each calculate one temperaturedistribution, while ten temperature distributions are ascertained in onecycle for ten values of the H field strength in the case of sim_hr.exe(the “H controller”).

A transfer parameter (or input value from the point of view of thesimulator) “MODE” having the value MODE=1, the “H selection” (sim_h.exe)MODE=2 and the “H controller” (sim_hr.exe) MODE=3 is assigned to thecalculation type “T selection” (sim_t.exe).

The text below provides a more precise description, by way of example,of the three simulator program modules (which can each also be availableas standalone programs) with their functionalities.

H Selection (MODE=2, sim_h.exe)

A schematic illustration of the cycle of the program module sim_h.exe isshown in FIG. 3. The absolute value of the H field strength H isprescribed. The temperature distribution T(x,y,z) is sought.

First, the nanodeposit volume (deposit volume) or nanoparticle volume(“V_NP”) and an average HU value “HU_aver” from all the values HU(x,y,z)in V_NP are formed from the evaluation of the geometric nanoparticledistribution (read in via LV.raw) and comparison of the Hounsfield units(“HU(x,y,z)”, read in via CT.raw).

Next, the average iron concentration is estimated from the average“HU_aver”, and from this the iron mass “m_fe” (more generally: theparticle mass) in the V_NP is estimated, it being assumed in this casethat the nanoparticles have an iron core as the magnetically effectivecomponent. This approach is thus based on multiple approximations: m_feis estimated from the average iron concentration, which is in turnestimated from the average HU value. The concentration of thenanoparticles following instillation into the patient is determined fromthe CT data. Only approximately 50% of the instilled particles remain inthe body and are present in the “nanodeposit” in concentrations ordistributions which are difficult to foresee.

Independently of this, the simulator derives the iron core SAR “SAR_fe”from the H field strength. This is a reference power absorption rate inunits of W/g, for example, based on the iron mass, which indicates ameasured power absorption for the nanoparticles in an undilutedreference state, that is to say the power absorption rate of anundiluted magnetic fluid (undiluted batch) containing the nanoparticlesprior to instillation into a patient, for example.

The nanoparticles may be magnetic (that is to say ferromagnetic orferrimagnetic, for example), paramagnetic and/or superparamagnetic.Depending on parameters such as material, size distribution, etc., theremay be a mixture of different magnetic properties.

The SAR_fe is calculated by applying a nonlinear characteristic curveSAR_fe=f(H), determined metrologically beforehand for the magneticfluid. The characteristic curve is determined for a specific applicatorand specifically used nanoparticles (magnetic fluid). It is assumed thatthe treatment area is situated centrally between pole pieces of theapplicator (e.g. in an area of <+/−10 centimeters), so that, in a goodapproximation, the same (maximum) H field strength value can be usedeverywhere in that area. In order to avoid a complex tabularrepresentation, the characteristic curve SAR_fe=f(H) can be approximatedby three fitting factors a,b,c, so that it can assume a formSAR_fe=aH ^(b) +c  (1)with the units SAR_fe in W/g (watts per gram) and H in kA/m.

m_fe, SAR_fe and V_NP are now used to estimate an average “SAR_aver” forthe volume SAR(x,y,z) based on the tissue mass in W/kg (or,equivalently: multiplied by the specific local density in W/m³ or W/cm³)in the nanodeposit. This is thus an average power absorption density inthe deposited state of the nanoparticles.

Next, a location-dependent volume SAR distribution is formed, and theinvention assumes with good approximation that in V_NP the SAR valuesare proportional to the HU values, i.e.SAR(x,y,z)=HU(x,y,z)*SAR_aver/HU_aver in V_NP  (2),SAR(x,y,z)=0 outside V_NP  (3).

All the values HU(x,y,z) are positive in V_NP, which means that nophysically impossible negative SAR values can arise.

With the location-dependent power absorption “density” distributionSAR(x,y,z) as the source of the temperature, a BHTET(x,y,z)=f(SAR(x,y,z)) is then numerically solved, e.g. on the basis ofNadobny et al. 2007, equations (1)-(2), and in this case a finitedifference method with explicit temperature gradient calculation basedon Nadobny et al. 2007, equations (8)-(15), is applied.

The BHTE describing the model is dynamic, i.e. time-dependent. Aftersome time, a steady state condition is reached, in which the supply ofheat by power absorption in the applied magnetic field is the same asthe heat dissipation for the blood flow, cooling in the environment,perfusion terms. On the basis of experience, such a condition is reachedafter approximately 20 min. It may be prescribed in the simulator that,taking account of an appropriate safety margin, the steady statecondition is meant to be deemed to have been reached after 30 min(minutes), for example (hyperthermia treatment can take between 1 hourand 1.5 hours, for example). In principle, the user may also be treatedin the initial phase (before a steady state is reached). The simulatoroperates in a time domain, and can therefore also model and provide(output) any times before a value of 20 min or 30 min.

The H selection option (sim_h.exe) (MODE=2) requires just a single passin order to numerically solve the BHTE T(x,y,z)=f(SAR(x,y,z)).

The invention proposes that in the case of the H selection thetemperature is not limited, i.e. depending on the level of the H fieldstrength, any temperatures can arise in the body which may also be abovethe usual temperature limit values (e.g. 43° C. in healthy tissue).

T Selection (MODE=1, sim_t.exe)

FIG. 4 schematically shows a program cycle for the simulator in Tselection mode. Temperature limit values T_limit(x,y,z) are prescribed,and the field strength value H and the associated temperaturedistribution T(x,y,z) are sought.

First of all, the nanoparticle distribution is read in from LV.raw andthe Hounsfield units are read in via CT.raw—as in the case of sim_h.exe.Next, the iron concentration (generally the magnetically effectiveparticle concentration) is determined and from this the iron mass m_fe(generally the magnetically effective particle mass) is determined, asin the case of sim_h.exe. These variables are independent of an acquiredH field strength.

Further progression of sim_t.exe is different than in the case ofsim_h.exe, however, since the absolute value of the field strength isnot prescribed, but rather is now sought. The procedure comprises thecalculation of an appropriate volume SAR (in W/kg, or optionally as apower absorption density in W/m³) which “fits” the prescribed limittemperature, and also finding the H field strength from the calculatedvolume SAR.

The limit temperature selections T_limit(x,y,z) that need to be observedsimultaneously and with equal authorization in the example describedhere are as follows:

-   -   for the “non-PTV region” (for a Boolean operation, “body volume        minus PTV”), a maximum admissible temperature value        (=temperature limit value) T_limit(non-PTV) is prescribed. By        way of example, this value may have been set to 43° C. by        default in the main program of the simulation software and        transferred to the simulator thus, i.e. T_limit(non-PTV)=43° C.        The “non-PTV” region corresponds roughly to the healthy tissue        or the tissue that is not to be treated.    -   The temperature everywhere in the body volume is meant to be no        more than 80° C. This value may have been stipulated internally        in the simulator, for example. In interaction with the above        limit value, on the basis of which the non-PTV is meant to be at        no more than 43° C., this is a limit for the treatment area PTV,        and therefore T_limit(PTV)=80° C.

The resultant field strength H must observe both selections, i.e. it ismeant to be low enough for all the temperatures T(x,y,z) to be less thanor equal to 80° C. in the PTV and at the same time less than or equal to43° C. outside the PTV. In other words, of two field strength valueswhich each observe one of the two T selections, the lower is output.

To observe these T selections, it would theoretically be possible tostart sim_h.exe multiple times and to recurrently readjust the inputfield strength, so that the T selections would be observed at the end ofsuch an iterative process. This iterative and accordingly imprecise andcomplex path (cf. “iterative way” in Nadobny et al. 2007, page 1841) isnot pursued in this case.

The procedure according to the invention is different in this case inorder to determine the H field strength value directly, in aresource-saving manner and nevertheless precisely. In this case, itshould be borne in mind that the statement of problem is in no waytrivial, since the SAR (and hence temperature) is dependent on the Hfield strength in a nonlinear manner. However, a linear relationship canbe indicated at least for some of the statement of problem, specificallybetween the SAR and the temperature (cf. “decomposition way” in Nadobnyet al. 2007, page 1841, equations (5a), (5b), (6)). Thus, thetemperature distribution T(x,y,z) is first of all split into a basalcomponent T0(x,y,z), which is obtained without SAR, and into atemperature rise component T_rise(x,y,z)=K*ΔT(x,y,z), with the resultthat:T(x,y,z)=T0(x,y,z)+K*ΔT(x,y,z),  (4)and for the SAR the following is trueSAR(x,y,z)=K*ΔSAR(x,y,z).  (5)

K is a scalar scaling factor (in MODE=1, we refer to “temperature-based”scaling factor), ΔT(x,y,z) is the relative temperature increment andΔSAR(x,y,z) is the relative SAR distribution, which can also be calledthe relative power absorption density or is equivalent thereto. Unlikein the case of sim_h.exe, an absolute value SAR_aver is not determinedfrom SAR_fe, but rather an arbitrary (relative) test average “ΔSAR_aver”for the relative volume SAR ΔSAR(x,y,z) is prescribed internally. By wayof example, this test average may be set as ΔSAR_aver=100 W/kg in thesimulator. In a manner similar to that in equation (2), the inventionapproximates the location-dependent ΔSAR(x,y,z) values therefrom asfollows:ΔSAR(x,y,z)=HU(x,y,z)*ΔSAR_aver/HU_aver in V_NP,  (6)ΔSAR(x,y,z)=0 outside V_NP.  (7)

Next, two passes for numerically solving the BHTE are started insuccession: once for T0(x,y,z) (with SAR(x,y,z)=0) and once for therelative temperature increment ΔT(x,y,z)=f(ΔSAR(x,y,z)). Next, thetemperature-based scaling factor is found using a minimum search overall support points or voxels x,y,z:K=Min(T_limit(x,y,z)−T0(x,y,z))/ΔT(x,y,z)),  (8)where T_limit(x,y,z)=80° C. in the non-PTV and 43° C. in the PTV.

When the temperature-based scaling factor K has been found, it isimmediately possible to indicate the absolute SAR(x,y,z) and T(x,y,z)using equations (5) and (4) without the BHTE needing to be numericallysolved once again. For the average of SAR(x,y,z), the following islikewise true:SAR_aver=K*ΔSAR_aver.  (9)

Next, the invention performs the initial steps from sim_h.exe in inverseorder: first, the iron core SAR SAR_fe is determined from the averageSAR_aver and the previously determined iron mass m_fe. The last step isthe application of a generally nonlinear characteristic curveSAR_fe=f(H) in the inverse sense, i.e. the value SAR_fe is used toascertain the H field strength value, which is then finallycommunicated—in addition to the temperature distribution T(x,y,z)—to theuser as an output.

H Controller (MODE=3, sim_hr.exe)

FIG. 5 schematically shows a program cycle for the simulator in Hregulator mode. In a call, the simulator program sim_hr.exe immediatelysimulates a plurality of temperature distributions for a multiple Hselection, i.e. for different absolute values of H which are madeavailable as input values.

In the example described here, a set of ten temperature distributions isdetermined for a set of ten firmly prescribed field strength values (5,6, 7, 8, 9, 10, 11, 12, 13 and 14 kA/m). However, the H controller isnot simply a sim_h.exe executed ten times in succession with ten passesfor numerically solving the BHTE. In order to save computation time,instead only two passes—in a manner similar to that in sim_t.exe—arerequired, namely likewise for T0(x,y,z) and ΔT(x,y,z).

First of all, the evaluation of the geometric nanoparticle distribution(read in via LV.raw) and comparison of the Hounsfield units (read in viaCT.raw) are used—in a manner similar to that in the case of sim_h.exe—todetermine the average HU value HU_aver, from this the average particleor iron concentration and from this the particle or iron mass m_fe.These values are the same for all H values.

Next, for a relative value of ΔSAR_aver=100, ΔSAR_aver is estimated andΔSAR(x,y,z) is approximated—as in sim_t.exe—on the basis of equations(6) and (7) above.

In a manner similar to that in sim_t.exe, two passes are next performedfor the purpose of numerically solving the BHTE for T0(x,y,z) andΔT(x,y,z).

Next, a loop having i=10 repetitions starts. Each time, the relative Hfield strength value H(i) is read in, that is to say 5 . . . 14 kA/m in1 kA/m steps, for example. By applying the characteristic curveSAR_fe=f(H) based on equation (1), the value is used—as in the case ofsim_h.exe—each time to calculate the relevant value SAR_fe(i), and thisvalue (and the initially determined iron mass m_fe) is used to estimatethe absolute average of the volume SAR, SAR_aver(i).

Hence, for each loop repetition, a scaling factor K(i) can be determinedaccording to the invention from (according to the invention, we refer inMODE=3 to “power-absorption-based” scaling factor):K(i)=SAR_aver(i)/ΔSAR_aver.  (10)

With the power-absorption-based scaling factor K(i), an absolutetemperature distribution is obtained for each i:T(x,y,z,i)=T0(x,y,z)+K(i)*ΔT(x,y,z),  (11)and is then output. Hence, ten absolute (scaled) temperaturedistributions are output in succession, although only two passes wererequired for numerically solving the BHTE.

It is pointed out that the power-absorption-based scaling(power-absorption-based scaling factor K as per equation 10) performedin the case of the H controller (MODE=3) is based on the specific powerabsorption rate (SAR) and is therefore fundamentally different than thescaling which is performed in the T selection (MODE=1) on a (limit)temperature basis (temperature-based scaling factor K as per equation8). In addition, it is noted that the power-absorption-based scalingfactor K, in the case of the H controller (equation 10), cannot simplybe formed by forming a ratio of H values, as is explained by thegenerally present nonlinearity of the characteristic curve SAR_fe=f(H).

Binary Input File CT.Raw

This 3D data record can represent a regular 3D grid, the elements(pixels) of which have associated CT density values (Hounsfield units,as “short” numbers). The geometric reference to the position (x,y,z) ofan element in the CT grid is obtained via the x,y,z index and thestatements relating to the bounding box which are additionallytransferred to the simulator. The x coordinate is the fastest changing(inner loop), and the z coordinate is the slowest (outer loop). The CTdata record must correspond to the postoperative planning CT and containnanoparticles (following instillation of the magnetic fluid). Thesimulator is interested particularly in the CT values in thenanoparticle pixels. For these values, the simulator derives theinformation about the current iron concentration (that is presentfollowing the instillation in the body), which is relevant to thecalculation of the SAR and/or the temperature.

Binary Input File LV.raw

This data record (LV stands for “labeled volume”) can likewise representa regular 3D grid, the elements of which have associated coded labels(“byte” numbers). The geometric reference to the 3D position (x,y,z) ofan element in the LV grid is obtained via the index and the statementsrelating to the bounding box. The bounding box of LV.raw may beidentical to that of CT.raw. The data record LV.raw is produced in theprevious program main step “Segmentation”, for example on the basis ofthe planning CT. The labels are used to describe/code the followingthree types of regions:

-   -   Geometric-anatomical regions (exterior, “head”, tumor): This        information is required in order to perform the calculation of        the temperature distribution. By way of example, the simulator        models the thermal interface between the body and the exterior,        and therefore the relevant geometry must be known. It is also        expected that the doctor plots the tumor, even though the        simulator can also calculate without a segmented tumor. A        distinction is drawn between body (part) volumes, that is to say        “head”, for example, treatment volumes (PTV), that is to say        “tumor+border around the tumor”, for example, and deposit        volumes, that is to say one or more (many) voxels containing        nanoparticles. “non-PTV” would thus be “head minus tumor (incl.        border)” in the example.    -   Nanoparticle areas (source volumes): Geometric positions (marked        as labels) of the nanoparticles or deposits of the magnetic        fluid which are latched to the tissue (“nanodeposits”, deposit        volumes). These nanoparticle positions need to be communicated        to the simulator as an input in order to determine the SAR. The        SAR is produced only in the positions of the nanoparticles. The        nanoparticle areas can overlap the geometric-anatomical regions        and the thermal limit condition regions. The geometric        distribution of the nanoparticles inside the deposit volume or        the deposit volumes is important for the calculations, for        example in the T solver or H controller.    -   Thermal limit conditions regions: Regions or organs where        particular temperatures T_limit(x,y,z) are not meant to be        exceeded. By way of example, a distinction is drawn between the        treatment area (“PTV”, “planning target volume”) and the rest of        the head for the healthy tissue (“non-PTV region”, i.e. region        outside the treatment area). The limit condition regions can        overlap the geometric-anatomical regions and the nanoparticle        areas. As a standard option, the segmentation editor provides        the tumor plus a tumor border of 1 cm as PTV.

The coding of LV.raw is implemented such that, for each 8-bit label, 6bits are used for coding the geometric-anatomical information, and 1 bitis used for coding the nanoparticles (YES/NO) and the PTV (YES/NO),respectively.

A second exemplary embodiment of the inventive simulation tool isdescribed more precisely below. A program package implements atemperature simulator (occasionally also called just “simulator” below)which is part of a piece of simulation software which has been developedwith the aim of providing assistance in cancer therapies. Like the firstexemplary embodiment described above, the second exemplary embodiment isalso provided for simulations in the head area.

As a result of the introduction of the magnetic fluid into the tumorarea (“instillation” or “implantation”), what are known as “nanoparticledeposits” or “nanodeposits” can be found in this area. During thetherapy, these nanoparticles can be activated by means of high-levellow-frequency external magnetic fields, i.e. the influence of themagnetic field can result in a local temperature rise. On the basis ofthe CT data, the simulator produces a simulation (protection) of thetemperature distribution in the head area on the basis of the magneticfield strength of the therapy appliance (magnetic field activator). Thisoccurs after the instillation but before the therapy. The resultsprovided by the simulator are neither a prerequisite for therapy beingable to be performed nor binding for the performance of said therapy,for example in relation to a particular application field strengthcalculated by the simulator. By way of example, the performance of thetherapy can be influenced by a temperature measurement carried outduring the therapy. This temperature measurement is more crucial innature for the doctor than simulation results. Simulation resultsand—crucially—temperature measurement can provide the doctor withpointers for assessing the therapy.

In a manner similar to that in the first exemplary embodiment, thesimulator performs the following main tasks, inter alia:

-   -   simulation of a three-dimensional temperature distribution, as        is probably obtained through the application of the magnetic        field to nanoparticles on the assumption of a simplified        physical model;    -   estimation of a magnetic field strength (H field strength) on        the basis of particular temperature selections for the patient        model.

In a manner similar to that in the first exemplary embodiment, thesimulator is not part of a main program core in this case, but rather islinked to the main program core as part of an external SOUP (“Softwareof Unknown Provenance”). However, the data interchange between thesimulator and the main program core no longer takes place via anexternal rigid directory interface (complete writing out and reading inof the data via hard disk), but rather the simulator is linked as aprogram library and therefore the data interchange takes place via mainmemory. Important changes which arise as a result of this differentsoftware structure or architecture include:

-   -   linking of the simulator as a program library instead of        execution programs such as sim_t.exe, sim_h.exe and sim_hr.exe;    -   introduction of the “fast H controller” instead of the “H        controller”;    -   dynamic memory location management for the FORTRAN arrays.

The modification of the H controller is described in detail furtherbelow. Furthermore, the linking as a program library will now be dealtwith briefly.

In the case of the first exemplary embodiment, the simulator comprisedthree separate FORTRAN execution programs “sim_t.exe”, “sim_h.exe” and“sim_hr.exe”, and each program was responsible for a particularselection mode (modes 1, 2 and 3). Accordingly, a transfer parameter (orinput value from the point of view of the simulator) “MODE” with thevalue MODE=1 was transferred to the calculation type “T selection” (“T”for “temperature”; sim_t.exe program), MODE=2 was transferred to the “Hselection” (“H” stands for “magnetic”, i.e. selection of an H fieldstrength; sim_h.exe program), and MODE=3 was assigned to the “Hcontroller” (multiple desired field strength values, sim_hr.exeprogram).

In the second exemplary embodiment which is discussed here, the basicsplit of the program into three modes is retained (MODE=1, MODE=2 andMODE=3), with MODE=3 having been modified in terms of software (cf.further below). However, the simulator no longer comprises threeseparate exe programs, but rather acts as a program library. Hence, thecommunication or the data interchange between the main program core andthe simulator no longer takes place via external directory interfaces,but rather takes place via main memory. Advantages of this solution are,inter alia, that it is no longer necessary for large volumes of data tobe read out and in via external directories (i.e. from or to a harddisk), and thus the program execution speeds are increased and/orpossible sources of error are eliminated.

The simulator implemented as a program library can be called from themain program core. The main program of the program library forms aninterface to the main program core, that is to say takes on the tasks ofthe external interface in the first exemplary embodiment—which interfaceensures the provision of CT.raw, LV.raw in said embodiment—at the “SOUPend”.

All the main programs in the first exemplary embodiment, i.e. sim_t.exe(for MODE=1, T selection), sim_h.exe (for MODE=2, H selection) andsim_hr.exe (for MODE=3, H controller), are provided as FORTRAN (main)subroutines in the second exemplary embodiment which are then all calledsequentially (depending on the MODE) by the main program. Thesecustomized main subroutines are referred to here as “mainsubroutines”.The order in which the simulator modes are called and the simulator dataare managed continues to be controlled and managed from the main programcore.

The following main subroutines exist:

-   -   “mainsubroutine_sim_t_voxel_win” (for MODE=1, T selection),    -   “mainsubroutine_sim_h_voxel_win” (for MODE=2, H selection), and    -   “mainsubroutine_sim_hr_voxel_win” (for MODE=3, fast H        controller).

The CT data and the segmented label data are not, as in the firstexemplary embodiment, written to the hard disk by the main program coreas binary data records CT.raw (CT data record) and LV.raw (data recordwith coded labels) and then read in by the hard disk again as a binaryinput for the simulator. Instead, these data are in this casetransferred as arrays in the main memory via the simulator.

A text file SimInput.txt (this provides the input for the firstexemplary embodiment of the simulator) is not needed in this case. Allthe input parameters are carried in argument lists from the main programcore to the main program of the simulator and on to the threemainsubroutines. This relates to the following input parameters, interalia: MODE (1,2,3), desired H field strength (H=magnetic field strength,indicated in kA/m), desired maximum temperature in the non-PTV area,molarity and fitting factors a,b,c (profile of the characteristic curveSAR_fe=f(H)), all the CT file dimension parameters (number of elements,bounding box, statements relating to the coordinate system used (m, cm,mm), number of regions).

There are also no output temperature data records written from thesimulator to the hard disk and then read into the main program coreagain in this case. Instead, appropriate arrays are transferred from themain program of the simulator to the main program core via main memory.

All the output parameters calculated in the first exemplary embodimentof the simulation program and then written to the output fileSimOutput.txt are in this case transferred from the simulation programvia argument lists from the mainsubroutines to the main program of thesimulator and on to the main program core via main memory. The mostimportant output parameters are: MODE (1,2,3), resultant H fieldstrength, resultant maximum temperature in the non-PTV area, resultantmaximum temperature in the PTV area, percentage share tumor withresultant temperatures>39 degrees, nanoparticle volume, tumor volume,resultant average volume SAR, resultant average Hounsfield unit value inthe nanoparticle volume, average iron core SAR.

The following additional parameters are transferred in the argumentlists of the mainsubroutines to the main program of the simulator: errormessages, and parameters which monitor the order of the calls to themainsubroutines (MODE=3 must not be called before MODE=1, see below).

Overview of the Cycles in the Second Exemplary Embodiment

The simulator provides the user with two options for ascertaining theabsolute temperature distribution:

-   -   by means of the H selection: The absolute value of the H field        strength is prescribed (in kA/m); the temperature distribution        T(x,y,z) (in ° C.) is sought; and    -   by means of the T selection: the temperature limit values        T_limit are prescribed (in ° C.), and the field strength value H        (in kA/m) and the associated temperature distribution T(x,y,z)        (in ° C.) are sought.

The T selection (MODE=1) is implemented by means of the call to themainsubroutine mainsubroutine_sim_t_voxel_win. The H selection (MODE=2)requires the other mainsubroutine mainsubroutine_sim_h_voxel_win to becalled. The fast H controller (MODE=3), which is also an H selection inprinciple, is implemented by means of the call to the mainsubroutinemainsubroutine_sim_hr_voxel_win, but requires at least one call tomainsubroutine_sim_t_voxel_win beforehand, since it uses the output frommainsubroutine_sim_t_voxel_win as input.

The chronological sequence of the calls to the simulator programs iscontrolled or managed from the main program core and is effected on thebasis of a fixed scheme, which is shown in FIG. 6.

It is imperatively stipulated that after a changeover from the GUIsegmentation editor to the GUI temperature simulation editor, the MODE=1is always automatically started first, i.e. the T selection subroutinemainsubroutine_sim_t_voxel_win, i.e. without this requiring or needingan input by the user. This program observes two fixed limit temperatureselections:

-   -   maximum 43° C. outside the PTV (“non-PTV 43° C. limit”), and    -   maximum 80° C. everywhere else, i.e. de facto inside the PTV        (“whole-body 80° C. limit”).

In relation to the non-PTV 43° C. limit, it is noted that this value isregarded as a threshold temperature, above which damage to the healthytissue can increasingly occur. The whole-body limit of 80° C. can alsobe chosen differently, for example a value in the range between 50° C.and below 100° C., preferably between 60° C. and 90° C., can be chosen.Of the two limit conditions, the one which occurs at the lower H fieldstrength becomes effective.

After the performance of the first, i.e. automatic, pass in MODE=1, themain program core examines internally (without an output being providedby means of GUI) what the level of the H field strength value that isoutput internally as an output from the main program of the simulatorprogram library is. If this value is greater than 15 kA/m, a pass inMODE=2 again starts automatically with the H selection 15 kA/m, orgenerally the physically maximum settable H field strength value on themagnetic field activator. If the output value from MODE=1 is less thanor equal to 15 kA/m, it is not necessary for a program run to be startedin MODE=2, and the next step follows directly.

Following the termination of the automatic passes as described above,the following data are output to the GUI:

-   -   temperature distribution;    -   H field strength recommendation, i.e.        -   the output from MODE=1 when H<15 kA/m, or        -   the value 15 kA/m (limiting pass in MODE=2 was performed),    -   maximum temperature reached outside PTV, maximum temperature        reached in the whole calculation area, and further variables.

There may be cases in which the maximum temperature outside the PTV islower than the maximum admissible temperature (e.g. 43° C.),specifically when the limit condition of, by way of example, 80° C. hasbeen reached in the PTV.

After considering this initial result (the result from the automaticpasses) on the monitor, the user decides whether he is satisfied withthe result. If this is not the case, he can type in any desired H fieldstrength value and start a fast calculation of the temperaturedistribution in MODE=3 (fast H controller) for this value as often asdesired.

The fast H controller has no limitations at all in respect of thetemperatures reached, unlike the T selection in the initial pass.Therefore, there may be cases in which the temperatures reached arehigher (or lower) than 80° C. in the calculation area and/or higher(and/or lower) than 43° C. outside PTV.

The user can return to the program main step “segmentation” at any timein order to make segmentation corrections, such as corrections on thePTV. In this case, the simulator changes to the initial state. As soonas there are new LV data present as the output for the segmentation andthe user changes from the program main step “segmentation” to thetemperature calculation editor, the procedure described above isrepeated, i.e. the simulator starts with the initial pass “T selection”,etc. Alternatively, the initial pass in MODE=1 can be initiated withoutreturning to the segmentation editor by using a GUI button “Restartautomatic temperature simulation”. In this case too, the procedure inwhich the simulator starts with the initial pass “T selection”, etc., isrepeated.

The three simulator modes are described below according to their aimsand functions.

MODE=2 (H selection, Call Goes to Mainsubroutine_sim_h_voxel_win)

The absolute value of the H field strength is prescribed. Thetemperature distribution T(x,y,z) is sought, cf. the cycle shownschematically in FIG. 7. First of all, the patient model is generated.This is based on the combination of two data records (arrays):

-   -   CT data record (array), which is read in by the simulation        program as part of patient data that are to be read in (in this        regard cf. FIG. 3; in this fundamental cycle, the first and        second exemplary embodiments are very similar);    -   LV data record (array) (“LV” stands for “labeled volume”), i.e.        a coded label array, which is produced by the simulation program        during segmentation (cf. FIG. 3).

The basis used for the patient model is the LV elements (“labels”), inwhich the following information is coded on a voxel basis:

-   -   the geometric-anatomical regions (“head”, “tumor”, etc.);    -   the thermally relevant regions (treatment area, also called PTV,        “planning target volume”);    -   geometric distribution of the nanoparticles (NP).

The evaluation of the NP distribution results in the ascertainment ofthe NP volume V_NP. The comparison with the CT data record then takesplace, with the average Hounsfield unit (HU) value “HU_aver” beingdetermined by forming an average for those values of HU(x,y,z) whichfall into the V_NP.

Next, the average HU_aver is used to estimate the average ironconcentration, and for this the average iron mass m_fe in the V_NP.

Independently, e.g. in parallel with these steps, the simulator derivesthe iron core SAR (“SAR_fe”) from the H field strength. This isaccomplished by applying a generally nonlinear characteristic curveSAR_fe=f(H) that has been determined experimentally for the magneticfluid used. In this case, it is assumed that the treatment area issituated centrally between the pole pieces of the magnetic fieldapplicator, where there is the same (maximum, constant) H field strengthto a good approximation. In order to avoid a complex tabularillustration, the characteristic curve SAR_fe=f(H) can be approximatedby three fitting factors a,b,c as follows, for example:SAR_fe=aH ^(b) +c  (12)with SAR_fe in W/g (watts per gram) and H in kA/m (kiloamps per meter).

m_fe, SAR_fe and V_NP are now used to estimate the average “SAR_aver” ofthe volume SAR(x,y,z) in the nanodeposit, cf. equation (2) in Gneveckowet al. 2004, for example.

Next, the location-dependent volume SAR distribution is formed, it beingassumed that in V_NP the SAR values are proportional to the HU values,i.e.SAR(x,y,z)=HU(x,y,z)*SAR_aver/HU_aver in V_NP  (13)SAR(x,y,z)=0 outside V_NP.  (14)

All values HU(x,y,z) are meant to be positive in V_NP so that nophysically impossible negative SAR values occur. This can be ensured asearly as in the segmentation editor, for example, by means ofappropriate filtering or threshold setting and can possibly be checkedagain in the simulator.

With the location-dependent SAR(x,y,z) as the source of the temperature,the BHTE T(x,y,z)=f(SAR(x,y,z)) is then numerically solved, cf. Nadobnyet al. 2007, equations (1)-(2). This can be solved by using a finitedifference method with explicit temperature gradient calculation, asdescribed in Nadobny et al. 2007, equations (8)-(15), for example.

Unlike in MODE=1 and MODE=3, MODE=2 the BHTE describing the model issolved for absolute values of the SAR which are derived from theabsolute H selection value. The H selection option MODE=2 thereforerequires—unlike in MODE=1 and MODE=3—just a single pass for numericallysolving the BHTE T(x,y,z)=f(SAR(x,y,z)).

In the case of the H selection, the temperature is not limited, i.e.depending on the H field strength it is possible for any temperatures toarise in the body, which are also above the usual temperature limitvalues (e.g. 43° C. in healthy tissue).

MODE=1 (T Selection, Call Goes to: mainsubroutine_sim_t_voxel_win)

Temperature limit values T_limit(x,y,z) are prescribed, and the fieldstrength value H and the associated temperature distribution T(x,y,z)are sought, cf. the schematic illustration in FIG. 8. In contrast to theT selection in the first exemplary embodiment, particular data recordsare output for the later call for MODE=3 (fast H controller) and aretemporarily provided in the main memory.

First of all, the patient model is produced—as in MODE=2—from the LV andCT data and then HU_aver and m_fe are determined for the NP volume(these variables are independent of the applied H field strength). Theremainder of the cycle in MODE=1 is different than in the case ofMODE=2, however, since the absolute value of the field strength is notprescribed but rather is sought. A procedure for ascertaining anappropriate volume SAR which corresponds to the prescribed limittemperature also has an additional step added for the application ofnanoparticles, namely the calculation of the H field strength from thevolume SAR.

The limit temperature selections T_limit(x,y,z) which need to beobserved simultaneously and with equal authorization are:

-   -   the “non-PTV” region corresponds approximately to the healthy        tissue or the tissue that does not need to be treated. For this        non-PTV region, which in the Boolean sense is “body minus PTV”        or “head minus PTV”, a maximum admissible temperature value        (=temperature limit value) T_limit(non-PTV) is prescribed. This        value may be able to be altered by the user, or else may be        firmly prescribed. The value may also have been set to 43° C. by        default, i.e. T_limit(non-PTV)=43° C.    -   The temperature in the body is not meant to exceed a maximum        value of 80° C., for example, anywhere. This value may be firmly        prescribed in the simulator, for example. Since the non-PTV is        meant to be at no more than 43° C. at the same time, the second        limitation is effective for the treatment area PTV, i.e.        T_limit(PTV)=80° C.

The resultant field strength H must observe both selections, i.e. it ismeant to be low enough for all the temperatures T(x,y,z) to be less thanor equal to 80° C. in the PTV, and at the same time to be less than orequal to 43° C. outside PTV. Of the two field strength values which eachobserve one of the two T selections, the lower is output.

To observe these T selections, it would be possible to start MODE=2multiple times, with the input field strength being repeatedlyreadjusted, so that the T selections would be observed at the end ofsuch an iterative process. This iterative and accordingly imprecise orcomplex path (cf. “iterative way” in Nadobny et al. 2007, page 1841) isnot pursued in this case.

The procedure according to the invention is different in this case inorder to determine the H field strength value directly, in aresource-saving manner and nevertheless precisely. In this case, itshould be borne in mind that the statement of problem is in no waytrivial, since the SAR (and hence temperature) is dependent on the Hfield strength in a nonlinear manner. However, a linear relationship canbe indicated at least for some of the statement of problem, specificallybetween the SAR and the temperature (cf. “decomposition way” in Nadobnyet al. 2007, page 1841, equations (5a), (5b), (6)). Thus, thetemperature distribution T(x,y,z) is first of all split into a basalcomponent T0(x,y,z) (as would be obtained without SAR) and into atemperature rise component T_rise(x,y,z)=K*ΔT(x,y,z), withT(x,y,z)=T0(x,y,z)+K*ΔT(x,y,z),  (15)where the following is true for the SAR:SAR(x,y,z)=K*ΔSAR(x,y,z).  (16)

K is a scalar scaling factor—that needs to be ascertained—(we refer to“temperature-based” scaling factor in MODE=1), ΔT(x,y,z) is the relativetemperature increment and ΔSAR(x,y,z) is the relative SAR distribution.

In contrast to the first exemplary embodiment, the output for amainsubroutine_sim_t_voxel_win comprises not only the actual temperaturedistribution T(x,y,z) but also distributions T0(x,y,z) and ΔT(x,y,z).These temperature distributions are characterized in detail as follows:

-   -   T(x,y,z): Absolute resultant temperature distribution, which        corresponds to a particular absolute SAR which is necessary in        order to comply with particular constraints, e.g. in MODE=1 the        limit temperatures are such constraints. T(x,y,z) is also output        in the simulation program in accordance with the first exemplary        embodiment. T(x,y,z) belongs to the data which are visualized in        the GUI (“graphical user interface”) of the simulation program.    -   T0(x,y,z): Absolute “basal” temperature distribution, as        obtained without SAR; initial temperature and basal temperature        for numerically solving the time-dependent BHTE are identical as        for the solution based on T(x,y,z). This temperature        distribution is temporarily stored in the main memory and is        then available as an input for the fast H controller.    -   ΔT(x,y,z): The distribution of the relative temperature rise        (=temperature increment), said distribution being obtained for        an arbitrary, firmly prescribed and/or user-defined SAR level        (“fixed value” in FIG. 8). Since it is not the temperature but        rather the temperature increment that is simulated in this case,        initial temperature and basal temperature for numerically        solving the time-dependent BHTE are equal to zero. This        temperature distribution is likewise temporarily stored in the        main memory and is then available as an input for the fast H        controller.

Unlike in MODE=2, an absolute value SAR_aver is not determined fromSAR_fe, but rather an arbitrary (relative) test average “ΔSAR_aver” forthe relative volume SAR ΔSAR(x,y,z) is prescribed internally as a “fixedvalue” (constant, cf. FIG. 8) (in the simulator, this test average isset as “ΔSAR_aver=100 W/kg). In a manner similar to that in equation(13), this is used to approximate the location-dependent ΔSAR(x,y,z)values as follows:ΔSAR(x,y,z)=HU(x,y,z)*ΔSAR_aver/HU_aver in V_NP,  (17)ΔSAR(x,y,z) outside V_NP  (18)

Next, two passes for numerically solving the BHTE are started insuccession: once for T0(x,y,z)=f(SAR=0) and once for the relativetemperature increment ΔT(x,y,z)=f(ΔSAR(x,y,z)). The scaling factor isthen found using a minimum search over all support points (voxels) x,y,z(“temperature-based scaling factor”):K=Min(T_limit(x,y,z)−T0(x,y,z))/ΔT(x,y,z)  (19)with T_limit(x,y,z)=80° C. in Non_PTV and 43° C. in PTV.

When the temperature-based scaling factor K has been found, the absoluteSAR (x,y,z) and T(x,y,z) can immediately be specified using equations(16) and (15) without having to numerically solve the BHTE again. Forthe average of SAR(x,y,z), the following is likewise true:SAR_aver=K*ΔSAR_aver.  (20)

The initial steps from MODE=2 are then performed in inverse order: i)the iron core SAR SAR_fe is determined from the average SAR_aver and thepreviously determined iron mass m_fe; ii) the nonlinear characteristiccurve SAR_fe=f(H) (cf. Gneveckow et al. 2004, FIG. 5) is applied in theinverse sense, i.e. the H field strength value is ascertained from thevalue SAR_fe. Said H field strength value is transferred as an outputfrom mainsubroutine_sim_t_voxel_win to the main program of the programlibrary and on to the main program core or to the GUI. Although thethree data records T(x,y,z), T0(x,y,z) and ΔT(x,y,z) form the output ofmainsubroutine_sim_t_voxel_win, only T(x,y,z) is passed on to the GUI.T0(x,y,z) and ΔT(x,y,z) are temporarily stored in the main memory inorder subsequently to be provided as an input formainsubroutine_sim_hr_voxel_win in MODE=3.

MODE=3 (“Fast H Controller”, Call is Made tomainsubroutine_sim_hr_voxel_win)

In MODE=3, a temperature distribution is ascertained for arbitrary Hselection in a matter of seconds without needing to solve the BHTE, forexample using a finite difference program. The prerequisite is thatT0(x,y,z) and ΔT(x,y,z) are read in as an input formainsubroutine_sim_hr_voxel_win. These temperature data records shouldalready be present in the main memory, i.e. MODE=1 has already beensuccessfully called and terminated at least once, for example, for agiven patient model, cf. FIG. 9.

First of all, the patient model is produced—as in MODE=1 or MODE=2—fromthe LV and CT data. Next, HU_aver and m_fe are determined for the NPvolume.

These variables are independent of the applied H field strength. Theaverage iron mass m_fe has already been calculated in MODE=1 (Tselection). As an alternative to recalculation, it is also conceivablefor the average iron mass m_fe to be kept in the main memory followingcalculation in MODE=1 (T selection) for access by MODE=3 (fast Hcontroller). Since the underlying patient model is always the sameregardless of the MODE, a calculation such as that for the average ironmass m_fe could also be relocated entirely, for example to thesegmentation step (cf. FIG. 1). However, local calculation of suchvariables may be advantageous, for example in respect of aspects such asa modular structure for the program package, performance of softwaretests, etc.

Independently of the calculation of the average iron mass, a fieldstrength value H that has been input by the user in the GUI is readin—in a manner similar to that in the case of MODE=2—and the relevantvalue of the iron core SAR SAR_fe is calculated by applying thecharacteristic curve SAR_fe=f(H). From this value and from thepreviously determined iron mass m_fe, the absolute average of the volumeSAR, SAR_aver, is then ascertained. At the same time, a relative fixedvalue of ΔSAR_aver=100 W/kg is prescribed—as in MODE=1—, the fixed valueΔSAR_aver in MODE=3 needing to be identical to the fixed value ΔSAR_averin MODE=1.

The invention therefore allows the scaling factor K to be determinedfrom (according to the invention, in MODE=3 we refer to the“power-absorption-based” scaling factor):K=SAR_aver/ΔSAR_aver.  (21)

The calculated power-absorption-based scaling factor K and the T0(x,y,z)and ΔT(x,y,z) which have previously been calculated in MODE=1 and arepresent in the main memory result in the sought temperature distributionT(x,y,z) when equation (15) is applied, said temperature distributionthen being output to the GUI. Since the BHTE does not need to be solvedin the case of the fast H controller, the method described above isexecuted by means of a standard processor in a matter of seconds.

It is pointed out that the power-absorption-based scaling performed inthe case of the fast H controller (MODE=3) (power-absorption-basedscaling factor K as per equation 21) is based on the specific powerabsorption rate (SAR) and is therefore fundamentally different than thescaling which is performed in the T selection (MODE=1) on a (limit)temperature basis (temperature-based scaling factor K as per equation19). In addition, it is noted that the power-absorption-based scalingfactor K cannot be formed simply by forming a ratio of H values in thecase of the fast H controller (equation 21), which is explained by thenonlinearity that is generally present in the characteristic curveSAR_fe=f(H).

The second exemplary embodiment which is described here involvescalculation of the basal temperature distribution and the relativetemperature increment distribution in MODE=1 (T selection) and provisionof said distributions in the main memory as a basis for the fast Hcontroller which is sometimes subsequently called (MODE=3). Otherexemplary embodiments are likewise conceivable. By way of example, thetwo temperature distributions could be calculated and provided in themain memory (only) when the fast H controller is first called. In thiscase, the simulator would react to the first call to the first Hcontroller more slowly, while the subsequent calls to the fast Hcontroller would then be reacted to very quickly, i.e. virtually withoutwaiting times, with the output of the temperature distribution thatresults from the input H field strength.

The second exemplary embodiment described here has two programcomponents for calculating a specific temperature distribution on thebasis of a prescribed or a user-defined H field strength value, namelyonce on the H selection (MODE=2) and furthermore on the (fast) Hcontroller. In another exemplary embodiment, it would also be possiblefor just the (fast) H controller to be provided. However, it is notedthat the H selection calculates a specific temperature distributionindependently of a basal temperature distribution and a relativetemperature increment distribution. The presence of two independentcalculation paths in one program package may be advantageous for testpurposes, for example, and for maintenance and further development.

One of the two paths of calculation may also be better suited in aspecific environment, e.g. a hardware environment, or according tospecific requirements. Thus, in one particular exemplary embodiment, thesimulator may be of configurable design, for example. An experienceduser would set the simulator such that, by way of example, basaltemperature distribution and relative temperature increment distributionare not written out in MODE=1 (T selection), so as to save main memory.From his experience and the output from the T selection (MODE=1), theuser already favors a particular H value anyway, which means that theresultant, final temperature distribution can be calculated by callingthe H selection once. Since the main memory requirement is limited inthis case, such a configuration is sometimes suitable for a hardwareconfiguration with accordingly limited resources too.

In yet another variant, the automatic start of the T selection (MODE=1)is dispensed with. Instead, the simulator awaits a user input. If thiscomprises an appropriate command, the calculation of the T selection canbegin, e.g. on the basis of the prescribed temperature limit values. Ifthe user input comprises an H field strength value, the H selection canbe started, or the fast H controller (this may in turn be dependent onthe available hardware resources). If the latter basal temperaturedistribution and relative temperature increment distribution are not yetavailable, they ought to be produced upon the first call.

Interfaces Between Temperature Simulator and Main Program Core

In the first exemplary embodiment, the input values for the simulatorwere transferred to the simulator via an external text directoryinterface from main program core. To this end, a text file SimInput.txtwas formed which had a particular firmly defined line-based structure.The input values were read from SimInput.txt when the exe programs werecalled. In the simulator described here (in the second exemplaryembodiment), SimInput.txt is no longer in existence and the data aretransferred via an internal interface between the main program core andthe simulator program library via main memory, i.e. the data aretransferred via an internal program interface.

At the simulator end, this interface is implemented by means of argumentlists. The simulator input is generated from main program core (forexample in C++) and transferred to the FORTRAN simulation library bymeans of argument lists. By way of example, the input arrays and inputparameters comprise:

-   -   1-D array with CT data, i.e. HU values;    -   1-D array with LV data, i.e. the segmented labels;    -   input parameters in argument lists, comparable with those from        the former text file SimInput.txt from the first exemplary        embodiment.

The simulator output is generated in the FORTRAN library and transferredto the main program core or the GUI by means of argument lists. By wayof example, the output arrays or parameters comprise:

-   -   1-D array with the temperature values T(x,y,z); and    -   output parameters in argument lists which may be required in the        GUI and for producing a therapy plan (some of these parameters        are written to an output file SimOutput.txt in the first        exemplary embodiment).

The 3D CT data can be represented in the form of dynamic 1D FORTRANarrays which can be read into the main program core in the “patientdata” step and in the “segmentation” step.

An array can represent the 3D CT data for a regular 3D grid, theelements (pixels) of which have associated CT density values(“Hounsfield units” values, i.e. “HU values”). The geometric referenceto the position (x,y,z) of an element in the CT grid is obtained bymeans of the x,y,z index and the statements relating to the bounding boxwhich are transferred to the simulator in the main memory from the mainprogram core. The CT data record has to correspond to the postoperativeplanning CT and contain image information about the nanoparticles(following instillation of the magnetic fluid). For the simulator,particularly the HU values in the nanoparticle pixels are of interest.From these values, the simulator derives information about the ironconcentration which is present in the body following the instillation.The information is relevant to the calculation of the SAR and then thetemperature.

A further array can represent coded “labeled volume” (LV) data (labels)that are generated as a result of the segmentation on the basis of theplanning CT. In the geometric service, this array maps a regular 3Dgrid, the elements of which have associated coded labels (numbers). Thegeometric reference to the 3D position (x,y,z) of an element in the LVgrid is obtained by means of the index and the statements relating tothe bounding box. The bounding box of such an LV data record or grid isidentical to that of the CT data record. The LV data (labels) are usedto describe the following three categories of regions:

-   -   geometric-anatomical regions (exterior, “head”, “tumor”): This        information is required for calculating the temperature        distribution. In particular, the thermal interface between the        body and the exterior is modeled in the simulator, and therefore        its geometry must be known. The tumor should also be        represented, although the simulator can compute even without a        segmented tumor. In a precise sense, the “head” region means        “head minus tumor”.    -   Nanoparticle areas (source volumes): geometric positions (marked        as labels) of the nanoparticles (“nanodeposits”) latched in the        tissue. These nanoparticle positions need to be communicated to        the simulator as an input for determining the SAR. The SAR is        produced only at the positions of the nanoparticles. The        nanoparticle areas can overlap the geometric-anatomical regions        and the regions with, for example, thermal limit conditions. If        segmented nanoparticle regions are not present, the program is        terminated.    -   Thermal limit condition regions: Regions (e.g. organs) in which        particular temperatures T_limit(x,y,z) are not meant to be        exceeded. In a second exemplary embodiment, a distinction is        drawn just between the treatment area (PTV) and the rest of the        head (non-PTV region), i.e. the region outside the treatment        area, hence the healthy tissue. The limit condition regions can        overlap the geometric-anatomical regions and the nanoparticle        areas. As a standard option, the PTV region can be produced in a        segmentation editor as a tumor plus a tumor border of a desired        width. If the segmented PTV region is not present, the program        is terminated.

The coding of the LV data can be implemented in the main step“segmentation” by using, for each element of an 8-bit label, 6 bits forcoding the geometric-anatomical information and 1 bit for coding thenanoparticles (YES/NO) and the PTV (YES/NO), respectively.

Re the Differences Between the First and Second Exemplary Embodiments

The first exemplary embodiment, described with reference to FIGS. 2-5,differs from the second exemplary embodiment, described with referenceto FIGS. 6-9, in the following aspects, inter alia:

-   -   linking of the temperature simulator as a program library (“SOUP        integration”);    -   other programming/implementation of the H controller or fast H        controller.

In the first exemplary embodiment, the temperature simulator isimplemented in the form of separate/autarkic execution programs. In thesecond exemplary embodiment, the temperature simulator is implemented asa program library. In this case, the temperature simulator programs arenot called as separate exe files via an external directory interface,but rather they need to be managed as methods of a program library thatis linked to the main program core by using an internal interface thatcan be actuated directly from the main program core. One of theadvantages of this solution is that it is not necessary for largevolumes of data to be written to or read in from the hard disk viaexternal directories (as in the case of the first exemplary embodiment).Hence, the comparatively slow hard disk write and read operations aredispensed with, which can raise the execution speed of the simulator. Apossible source of error is also eliminated in this manner.

In relation to the operation of the “H controller” in the firstexemplary embodiment in contrast to the “fast H controller” of thesecond exemplary embodiment, it is noted that the simple H controllerinvolves the numerical calculation of two temperature distributions,namely the basal temperature distribution and the relative temperatureincrement distribution, in a manner similar to that in MODE=1, bysolving the BHTE twice. A loop is then called with i=10 passes, forexample. For each pass, a predefined H field strength value H(i) servesas an input, so that calculations for a total of 10 H field strengthvalues, e.g. from 5 kA/m to 14 kA/m, are performed in 1 kA/m steps. Eachloop pass i=1 . . . 10 involves the determination of apower-absorption-based scaling factor K(i) by which the temperatureincrement distribution is multiplied. i=10 resultant temperaturedistributions are obtained, formed on the basis of the scaling schemedescribed further up. These i=1 . . . 10 temperature distributions arethen written in succession to an external hard disk directory. Fromthere, it can be loaded as required by the user.

Behind this approach to a solution, there is the assumption that the tendistributions (i=1 . . . 10) can be calculated in the background (in amanner similar to a batch job), while the user is looking at thetemperature distribution ascertained in the first automatic pass(Mode=1) on the screen. This means that the user loses no time and, whenhe is finished looking at the temperature distribution, has 10 furthertemperature distributions available which cover the entire H fieldstrength range that can be set. He can therefore change over betweenthese temperature distributions. If he is interested in a specific valuewhich lies between the firmly prescribed H(i) values, he could calculatea pass with Mode=2 (H selection) again for this value.

In the practical application, this requires a comparatively large amountof memory space on the hard disk for large data records, however, inorder to store all 10 distributions; a typical value in this case is inthe order of magnitude of 2 GBytes. Writing the 10 temperaturedistributions to the hard disk and reading them from the hard disk takesa correspondingly long time. Sequentially loading the temperature datarecords from the hard disk is associated with a series of operations,for example loading the contours, calculating a catheter spline, etc.,these possibly being comparable with a pass of the BHTE from the pointof view of resource consumption. Sometimes, the advantage of changingover between the different previously calculated data records is thusinexhaustible.

The “simple” H controller based on the first exemplary embodimentprovides only temperature distributions for firmly predefined H fieldstrengths. If a value between the predefined values is of interest, itis necessary to start a temperature simulator pass, e.g. using the Hselection (Mode=2). This means at least one numerical solution to theBHTE, that is to say a time-intensive calculation.

The second exemplary embodiment realizes a different approach for the Hcontroller, which in this case is called a “fast H controller”. This isdistinguished from the simple H controller by speed, since it does notrequire a numerical solution to the BHTE, but rather scales the provided(previously calculated in the T selection step) relative temperatureincrement distribution. In other words, fast H controller ascertains thepower-absorption-based scaling factor K for a single H field strengthvalue, and the respective temperature distribution is then formed on thebasis of the scaling approach described further up.

The fast H controller therefore performs no time-consuming and memoryspace intensive advance calculation of multiple (for example 10)distributions, and is therefore particularly suitable for large datarecords, for example. Any desired H field strength value can be set andcan be calculated in a resource saving manner. However, the fast Hcontroller requires a prior pass in Mode=1. The two temperaturedistributions calculated there need to be available to the fast Hcontroller as an input. Appropriate main memory space needs to beavailable.

The first exemplary embodiment of a simulator for a simulation tooltherefore tends to require longer for execution, and also requires morehard disk memory (this is usually not a problem for systems that arecustomary today), but requires less main memory. Hence, this simulatoris sometimes particularly suitable for execution on a PC, for example astandalone PC, or else a mobile computer such as a notebook or the like.By contrast, the second exemplary embodiment tends to work more quickly,particularly for processing H field strength values that are input bythe user, and requires less hard disk memory space, but a larger mainmemory. Hence, the second exemplary embodiment of a simulator for asimulation tool tends to be suitable for use on powerful computers suchas workstations or in mainframe systems.

Further modifications of the exemplary embodiments are conceivable whichresult in hybrid forms between the functionalities described for thefirst and second exemplary embodiments. It is thus conceivable for anexecutable file such as sim_t.exe from the first exemplary embodiment towrite the basal temperature distribution and the relative temperatureincrement distribution to the hard disk, from where it can then be readin again by another executable file, such as sim_hr.exe, as described inthe first exemplary embodiment. In this way, it is possible to provide a“fast H controller” without numerically solving the BHTE again, but thewriting and reading of the data to and from the hard disk might slowdown the cycle. Nevertheless, this modification may be advantageous forparticular software configurations, hardware configurations and/orapplications overall.

The invention is not limited to the exemplary embodiments described hereand the aspects highlighted therein; on the contrary, a large number ofmodifications that are within the scope of action of a person skilled inthe art are possible within the area indicated by the appended claims.

For the sake of completeness, the subject matter of the patentapplication establishing priority is repeated in briefly summarized formbelow:

1. A computer-aided simulation method for providing assistance inthermotherapy planning, wherein the thermotherapy comprises hyperthermiatreatment of a tumor volume in a body volume of a human body, whereinthe hyperthermia treatment comprises the application of a magnetic fieldin a treatment volume by means of a magnetic field applicator, whereinthermal energy can be introduced into at least one deposit volume bypower absorption in the applied magnetic field by means of magnetic,paramagnetic and/or superparamagnetic nanoparticles deposited in thebody, and wherein the method comprises the following steps:

-   -   in a first calculation step (“T selection”), calculation of a        field strength value that needs to be set on the applicator on        the basis of a geometric distribution of the nanoparticles and        at least one prescribed temperature limit value which is not        meant to be exceeded by the hyperthermia treatment;    -   in an optional second calculation step (“H controller”),        calculation, for each field strength value from a plurality of        prescribed field strength values, of a temperature distribution        that is to be expected for at least part of the body volume; and    -   provision of the calculated field strength value and optionally        the calculated temperature distribution in order to provide        assistance for the user in planning the thermotherapy.        2. The method according to subject 1, wherein the temperature        limit value or one of a plurality of temperature limit values        relates to a maximum temperature only within the treatment        volume that is to be heated.        3. The method according to subject 2, wherein the temperature        limit value relates to a prescribed temperature maximum in a        range from 60° C. to 100° C., preferably 70° C. to 90° C.,        particularly 80° C., in the treatment volume.        4. The method according to one of the preceding subjects,        wherein the temperature limit value or one of a plurality of        temperature limit values relates to a maximum temperature        outside the treatment volume that is to be heated.        5. The method according to subject 4, wherein the temperature        limit value relates to a prescribed temperature maximum in a        range from 40° C. to 45° C., particularly 43° C., outside the        treatment volume.        6. The method according to one of the preceding subjects,        wherein two temperature limit values which each relate to        different volumes are used in the first calculation step (“T        selection”).        7. The method according to one of the preceding subjects,        wherein the calculation result from the first calculation step        (“T selection”) is taken as a basis for automatically performing        a third calculation step (“H selection”) when the field strength        value calculated in the first calculation step (“T selection”)        is greater than a prescribed maximum field strength value,        particularly a maximum settable field strength value on the        applicator, wherein a temperature distribution that is to be        expected is calculated in the third calculation step (“H        selection”) for the prescribed maximum field strength value.        8. The method according to one of the preceding subjects,        wherein no temperature limit value is used in the calculations        in the second calculation step (“H controller”) and/or in the        third calculation step (“H selection”).        9. The method according to one of the preceding subjects,        wherein the calculations in the second calculation step (“H        controller”) are performed for a plurality of prescribed field        strength values that can be set on the applicator, preferably        between 3 and 20 field strength values, particularly preferably        between 5 and 10 field strength values.        10. The method according to one of the preceding subjects,        wherein the second calculation step (“H controller”) is        initiated after the first (“T selection”) and possibly third (“H        selection”) calculation steps by a user input.        11. The method according to one of the preceding subjects,        wherein output of the calculation results is followed by the        performance of a fourth calculation step (“H selection”), in        which a user input of field strength is accepted and, on the        basis of the accepted field strength, a temperature distribution        that is to be expected is calculated.        12. The method according to one of the preceding subjects,        wherein the calculation of the field strength value that is to        be set in the first calculation step (“T selection”) does not        comprise an iteration in which temperature distributions are        calculated from chosen field strength values so as to        iteratively arrive at the sought field strength value.        13. The method according to one of the preceding subjects,        wherein the first calculation step (“T selection”) has the        following steps:    -   calculation of an average power absorption density (“SAR_aver”)        in the applicator magnetic field in the deposit volume, wherein        a relative power absorption density is calculated on the basis        of a measured geometric distribution of the nanoparticles, a        prescribed bioheat transfer equation is solved precisely once in        order to obtain a basal temperature distribution without power        absorption, and the bioheat transfer equation is solved        precisely once in order to obtain a relative temperature        increment distribution on the basis of the relative power        absorption density; and wherein the relative power absorption        density is scaled by a scaling factor which is obtained on the        basis of the at least one prescribed temperature limit value,        the basal temperature distribution and the relative temperature        increment distribution;    -   calculation, on the basis of the calculated average power        absorption density and the calculated mass of the nanoparticles,        of a reference power absorption rate (“SAR_Fe”) which indicates        the specific power absorption rate of an undiluted magnetic        fluid containing the nanoparticles, for example; and    -   calculation of a field strength value on the basis of the        calculated reference power absorption rate and a prescribed        characteristic curve which relates to a relationship between        reference power absorption rate and applied field strength.        14. The method according to one of the preceding subjects,        wherein the calculation of the temperature distributions in the        second calculation step (“H controller”) comprises precisely two        calculations of temperature distributions regardless of the        number of prescribed field strength values.        15. The method according to one of the preceding subjects,        wherein the second calculation step (“H controller”) has the        following steps:    -   calculation of a relative power absorption density distribution        (“ΔSAR(x,y,z)”) and a relative average power absorption density        (“ΔSAR_aver”) on the basis of a measured geometric distribution        of the nanoparticles;    -   provision of a basal temperature distribution (“T0(x,y,z)”) on        the basis of a solution to a prescribed bioheat transfer        equation without power absorption, and provision of a relative        temperature increment distribution (“ΔT(x,y,z)”) on the basis of        a solution to the bioheat transfer equation with the calculated        relative power absorption density distribution (“ΔSAR(x,y,z)”);    -   performance of the following steps for each field strength value        from the plurality of prescribed field strength values:        -   calculation of a reference power absorption rate            (“SAR_Fe(i)”) which indicates the specific power absorption            rate of an undiluted magnetic fluid containing the            nanoparticles, for example, wherein the calculation is based            on the respective field strength value and a prescribed            characteristic curve which relates to a relationship between            reference power absorption rate and applied field strength;        -   calculation, on the basis of the reference power absorption            rate (“SAR_Fe(i)”) and the calculated mass of the            nanoparticles in the deposit volume, of an average power            absorption density (“SAR_aver(i)”);        -   calculation of a scaling factor (“K(i)”) on the basis of the            respective average power absorption density (“SAR_aver(i)”)            and the relative average power absorption density            (“ΔSAR_aver”);        -   calculation of a respective temperature distribution            (“T(x,y,z,i)”) on the basis of the basal temperature            distribution (“T0(x,y,z)”), the relative temperature            increment distribution (“ΔT(x,y,z)”) and the scaling factor            (“K(i)”).            16. A computer-aided simulation method (“T selection”) for            providing assistance in thermotherapy planning, wherein the            thermotherapy comprises hyperthermia treatment of a tumor            volume in a body volume of a human body, wherein the            hyperthermia treatment comprises the application of a            magnetic field in a treatment volume by means of a magnetic            field applicator, wherein thermal energy can be introduced            into at least one deposit volume by power absorption in the            applied magnetic field by means of magnetic, paramagnetic            and/or superparamagnetic nanoparticles deposited in the            body, wherein the method relates to the calculation of a            field strength that needs to be set on the applicator on the            basis of a geometric distribution of the nanoparticles and            at least one prescribed temperature limit value which is not            meant to be exceeded by the hyperthermia treatment (“T            selection”); wherein the method has the following steps:    -   calculation of an average power absorption density (“SAR_aver”)        in the applicator magnetic field in the deposit volume, wherein        a relative power absorption density is calculated on the basis        of a measured geometric distribution of the nanoparticles, a        prescribed bioheat transfer equation is solved precisely once in        order to obtain a basal temperature distribution without power        absorption, and the bioheat transfer equation is solved        precisely once in order to obtain a relative temperature        increment distribution on the basis of the relative power        absorption density; and wherein the relative power absorption        density is scaled by a scaling factor which is obtained on the        basis of the at least one prescribed temperature limit value,        the basal temperature distribution and the relative temperature        increment distribution;    -   calculation, on the basis of the calculated average power        absorption density and the calculated mass of the nanoparticles,        of a reference power absorption rate (“SAR_Fe”) which indicates        the specific power absorption rate of an undiluted magnetic        fluid containing the nanoparticles, for example;    -   calculation of a field strength value on the basis of the        calculated reference power absorption rate and a prescribed        characteristic curve which relates to a relationship between        reference power absorption rate and applied field strength; and    -   provision of the calculated field strength value for providing        assistance for the user in planning the thermotherapy.        17. A computer-aided simulation method (“H controller”) for        providing assistance in thermotherapy planning, wherein the        thermotherapy comprises hyperthermia treatment of a tumor volume        in a body volume of a human body, wherein the hyperthermia        treatment comprises the application of a magnetic field in a        treatment volume by means of a magnetic field applicator,        wherein thermal energy can be introduced into at least one        deposit volume by power absorption in the applied magnetic field        by means of magnetic, paramagnetic and/or superparamagnetic        nanoparticles deposited in the body, wherein the method relates        to the calculation, for each field strength value from a        plurality of prescribed field strength values, of a temperature        distribution that is to be expected for at least some of the        body volume (“H controller”); and wherein the method has the        following steps:    -   calculation of a relative power absorption density distribution        (“ΔSAR(x,y,z)”) and a relative average power absorption density        (“ΔSAR_aver”) on the basis of a measured geometric distribution        of the nanoparticles;    -   provision of a basal temperature distribution (“T0(x,y,z)”) on        the basis of a solution to a prescribed bioheat transfer        equation without power absorption, and provision of a relative        temperature increment distribution (“ΔT(x,y,z)”) on the basis of        a solution to the bioheat transfer equation with the calculated        relative power absorption density (“ΔSAR(x,y,z)”);    -   performance of the following steps for each field strength value        from the plurality of prescribed field strength values:        -   calculation of a reference power absorption rate            (“SAR_Fe(i)”) which indicates the specific power absorption            rate of an undiluted magnetic fluid containing the            nanoparticles, for example, wherein the calculation is based            on the respective field strength value and a prescribed            characteristic curve which relates to a relationship between            reference power absorption rate and applied field strength;        -   calculation, on the basis of the reference power absorption            rate (“SAR_Fe(i)”) and the calculated mass of the            nanoparticles in the deposit volume, of an average power            absorption density (“SAR_aver(i)”);        -   calculation of a scaling factor (“K(i)”) on the basis of the            respective average power absorption density (“SAR_aver(i)”)            and the relative power absorption density (“ΔSAR_aver”);        -   calculation of a respective temperature distribution            (“T(x,y,z,i)”) on the basis of the basal temperature            distribution (“T0(x,y,z)”), the relative temperature            increment distribution (“ΔT(x,y,z)”) and the scaling factor            (“K(i)”);    -   provision of the calculated temperature distributions in order        to provide assistance for the user in planning the        thermotherapy.        18. A computer program for carrying out the method according to        one of the preceding subjects when the computer program is        executed on a programmable computer device.        19. A data storage medium on which the computer program        according to subject 18 is recorded.        20. A computer device designed for providing assistance in        thermotherapy planning,        wherein the thermotherapy comprises hyperthermia treatment of a        tumor volume in a body volume of a human body,        wherein the hyperthermia treatment comprises the application of        a magnetic field in the treatment volume by means of a magnetic        field applicator,        wherein thermal energy can be introduced into at least one        deposit volume by power absorption in the applied magnetic field        by means of magnetic, paramagnetic and/or superparamagnetic        nanoparticles deposited in the body, and wherein the computer        device comprises the following components:    -   a first calculation component (“sim_t.exe”) designed to        calculate a field strength value that needs to be set on the        applicator on the basis of a geometric distribution of the        nanoparticles and at least one prescribed temperature limit        value which is not meant to be exceeded by the hyperthermia        treatment;    -   a second calculation component (“sim_hr.exe”), designed to        optionally calculate, for each field strength value from a        plurality of prescribed field strength values, a temperature        distribution that is to be expected for at least some of the        body volume; and    -   a component for providing the calculated field strength value        and optionally the calculated temperature distributions in order        to provide assistance for the user in planning the        thermotherapy.        21. A computer device designed for providing assistance in        thermotherapy planning, wherein the thermotherapy comprises        hyperthermia treatment of a tumor volume in a body volume of a        human body, wherein the hyperthermia treatment comprises the        application of a magnetic field in the treatment volume by means        of a magnetic field applicator, wherein thermal energy can be        introduced into at least one deposit volume by power absorption        in the applied magnetic field by means of magnetic, paramagnetic        and/or superparamagnetic nanoparticles deposited in the body,        wherein the computer device has a component (“sim_t.exe”) which        is designed to calculate a field strength that needs to be set        on the applicator on the basis of a geometric distribution of        the nanoparticles and at least one prescribed temperature limit        value which is not meant to be exceeded by the hyperthermia        treatment; and wherein the component (“sim_t.exe”) has the        following modules:    -   a module for calculating an average power absorption density in        the applicator magnetic field in the deposit volume, wherein a        relative power absorption density is calculated on the basis of        a measured geometric distribution of the nanoparticles, a        prescribed bioheat transfer equation is solved precisely once in        order to obtain a basal temperature distribution without power        absorption, and the bioheat transfer equation is solved        precisely once in order to obtain a relative temperature        increment distribution on the basis of the relative power        absorption density; and wherein the relative power absorption        density is scaled by a scaling factor which is obtained on the        basis of the at least one prescribed temperature limit value,        the basal temperature distribution and the relative temperature        increment distribution;    -   a module for calculating, on the basis of the calculated average        power absorption density and the calculated mass of the        nanoparticles, a reference power absorption rate which indicates        the specific power absorption rate of an undiluted magnetic        fluid containing the nanoparticles, for example;    -   a module for calculating a field strength value on the basis of        the calculated reference power absorption rate and a prescribed        characteristic curve which relates to a relationship between        reference power absorption rate and applied field strength; and    -   a module for providing the calculated field strength value in        order to provide assistance for the user in planning the        thermotherapy.        22. A computer device designed for providing assistance in        thermotherapy planning,        wherein the thermotherapy comprises hyperthermia treatment of a        tumor volume in a body volume of a human body, wherein the        hyperthermia treatment comprises the application of a magnetic        field in the treatment volume by means of a magnetic field        applicator, wherein thermal energy can be introduced into at        least one deposit volume by power absorption in the applied        magnetic field by means of magnetic, paramagnetic and/or        superparamagnetic nanoparticles deposited in the body, wherein        the computer device has a component (“sim_hr.exe”) which is        designed to calculate, for each field strength value from a        plurality of prescribed field strength values, a temperature        distribution that is to be expected for at least some of the        body volume; and wherein the component (“sim_hr.exe”) has the        following modules:    -   a module for calculating a relative power absorption density        distribution and a relative average power absorption density on        the basis of a measured geometric distribution of the        nanoparticles;    -   a module for providing a basal temperature distribution on the        basis of a solution to a prescribed bioheat transfer equation        without power absorption, and providing a relative temperature        increment distribution on the basis of a solution to the bioheat        transfer equation with the calculated relative power absorption        density distribution;    -   a module for performing the following steps for each field        strength value from the plurality of prescribed field strength        values:        -   calculation of a reference power absorption rate which            indicates the specific power absorption rate of an undiluted            magnetic fluid containing the nanoparticles, for example,            wherein the calculation is based on the respective field            strength value and a prescribed characteristic curve which            relates to a relationship between reference power absorption            rate and applied field strength;        -   calculation, on the basis of the reference power absorption            rate and the calculated mass of the nanoparticles in the            deposit volume, of an average power absorption density;        -   calculation of a scaling factor on the basis of the            respective average power absorption density and the relative            power absorption density;        -   calculation of a respective temperature distribution on the            basis of the basal temperature distribution, the relative            temperature increment distribution and the scaling factor;    -   a module for providing the calculated temperature distributions        in order to provide assistance for the user in planning the        thermotherapy.        23. A system, comprising a computer device according to one of        subjects 20 to 22 and a magnetic field applicator.        24. A system, comprising a computer program according to subject        18, a data storage medium according to subject 19, a computer        device according to one of subjects 20 to 22 or a system        according to subject 23, and also comprising a magnetic fluid        containing magnetic nanoparticles.        25. A method for controlled heating of an organ or tissue,        containing the steps of

-   A) introduction of magnetic, paramagnetic and/or superparamagnetic    particles into an organ volume or tissue volume,

-   B) ascertainment of the particle quantity and/or distribution in the    organ volume or tissue volume,

-   C) calculation of a field strength that is able to be set on the    basis of the method according to subject 1 or 16 or of a temperature    distribution on the basis of the method according to subject 17,

-   D) deposition of thermal energy by means of application of a    magnetic field, wherein the applied field strength corresponds to    the calculated field strength or to the field strength derived from    a calculated temperature distribution, in each case with a deviation    of +/−10%, preferably +/−5%, particularly +/−1%.    26. A method for treating a tumor in a patient, containing the steps    of

-   A) introduction of magnetic, paramagnetic and/or superparamagnetic    particles into a tumor volume,

-   B) ascertainment of the particle quantity and/or distribution in the    tumor volume,

-   C) calculation of a field strength that is able to be set on the    basis of the method according to subject 1 or 16 or of a temperature    distribution on the basis of the method according to subject 17,

-   D) deposition of thermal energy by means of application of a    magnetic field, wherein the applied field strength corresponds to    the calculated field strength or to the field strength derived from    a calculated temperature distribution, in each case with a deviation    of +/−10%, preferably +/−5%, particularly +/−1%.

The invention claimed is:
 1. A computer-aided simulation method forproviding assistance in thermotherapy planning, wherein thethermotherapy comprises hyperthermia treatment of a tumor volume in abody volume of a human body, wherein the hyperthermia treatmentcomprises the application of a magnetic field in a treatment volume bymeans of a magnetic field applicator, wherein thermal energy can beintroduced into at least one deposit volume by power absorption in theapplied magnetic field by means of magnetic, paramagnetic and/orsuperparamagnetic nanoparticles deposited in the body, and wherein themethod comprises the following steps: in a first calculation step (“Tselection”), calculation of a field strength value that needs to be seton the applicator on the basis of a geometric distribution of thenanoparticles and at least one prescribed temperature limit value whichis not meant to be exceeded by the hyperthermia treatment, wherein thefield strength value is calculated in the first calculation step (“Tselection”) on the basis of a prescribed characteristic curve whichindicates a relationship between (reference) power absorption rate andfield strength; in an optional second calculation step (“H controller”,“fast H controller”), calculation of the temperature distribution thatis to be expected for at least part of the body volume for each fieldstrength value from a plurality of prescribed field strength values,and/or a user-defined field strength value; and provision of thecalculated field strength value and optionally of at least onecalculated temperature distribution for the purpose of supporting theuser in planning the thermotherapy.
 2. The method as claimed in claim 1,wherein the temperature limit value or one of a plurality of temperaturelimit values relates to a maximum temperature only within the treatmentvolume that is to be heated, wherein preferably the temperature limitvalue relates to a prescribed temperature maximum in a range from 60° C.to 100° C., preferably 70° C. to 90° C., particularly 80° C., in thetreatment volume.
 3. The method as claimed in claim 1, wherein thetemperature limit value or one of a plurality of temperature limitvalues relates to a maximum temperature outside the treatment volumethat is to be heated, wherein preferably the temperature limit valuerelates to a prescribed temperature maximum in a range from 40° C. to45° C., particularly 43° C., outside the treatment volume.
 4. The methodas claimed in claim 1, wherein two temperature limit values which eachrelate to different volumes are used in the first calculation step (“Tselection”).
 5. The method as claimed in claim 1, wherein thecalculation result from the first calculation step (“T selection”) istaken as a basis for automatically performing a third calculation step(“H selection”) when the field strength value calculated in the firstcalculation step (“T selection”) is greater than a prescribed maximumfield strength value, particularly a maximum settable field strengthvalue on the applicator, wherein a temperature distribution that is tobe expected is calculated in the third calculation step (“H selection”)for the prescribed maximum field strength value.
 6. The method asclaimed in claim 1, wherein no temperature limit value is used in thecalculations in the second calculation step (“H controller”, “fast Hcontroller”) and/or in the third calculation step (“H selection”). 7.The method as claimed in claim 1, wherein the calculations in the secondcalculation step (“H controller”) are performed for a plurality ofprescribed field strength values that can be set on the applicator,preferably between 3 kA/m and 20 kA/m, particularly preferably between 5kA/m and 10 kA/m.
 8. The method as claimed in claim 1, wherein thecalculation of the field strength value that is to be set in the firstcalculation step (“T selection”) does not comprise an iteration in whichtemperature distributions are calculated from chosen field strengthvalues so as to iteratively arrive at the sought field strength value.9. The method as claimed in claim 1, wherein the first calculation step(“T selection”) has the following steps: calculation of an average powerabsorption density (“SAR_aver”) in the applicator magnetic field in thedeposit volume, wherein a relative power absorption density(“ΔSAR(x,y,z)”) is calculated on the basis of a measured geometricdistribution of the nanoparticles, a bioheat transfer equationdescribing the model is numerically solved precisely once in order toobtain a basal temperature distribution (“T0(x,y,z)”) without powerabsorption, and the bioheat transfer equation is numerically solvedprecisely once in order to obtain a relative temperature incrementdistribution (“ΔT(x,y,z)”) on the basis of the relative power absorptiondensity; and wherein the relative power absorption density(“ΔSAR(x,y,z)”) is scaled by a temperature-based scaling factor (“K”)which is obtained on the basis of the at least one prescribedtemperature limit value, the basal temperature distribution and therelative temperature increment distribution; calculation, on the basisof the calculated average power absorption density and the calculatedmass of the nanoparticles, of a reference power absorption rate(“SAR_Fe”) which indicates the specific power absorption rate of anundiluted magnetic fluid containing the nanoparticles, for example;calculation of a field strength value (“H”) on the basis of thecalculated reference power absorption rate (“SAR_Fe”) and a prescribedcharacteristic curve which relates to a relationship between referencepower absorption rate and applied field strength (“H”); and optionalcalculation of a respective temperature distribution (“T(x,y,z)”) on thebasis of the basal temperature distribution (“T0(x,y,z)”), the relativetemperature increment distribution (“ΔT(x,y,z)”) and thetemperature-based scaling factor (“K”).
 10. The method as claimed inclaim 1, wherein in the second calculation step (“fast H controller”),regardless of the number of prescribed and/or user-defined fieldstrength values, a provided (previously calculated in the firstcalculation step) basal temperature distribution (“T0(x,y,z)”) and/or aprovided (previously calculated in the first calculation step) relativetemperature increment distribution (“ΔT(x,y,z)”) are used.
 11. Themethod as claimed in claim 1, wherein in the second calculation step (“Hcontroller”), regardless of the number of prescribed and/or user-definedfield strength values, no more than two temperature distributions arecalculated, namely a basal temperature distribution (“T0(x,y,z)”) and/ora relative temperature increment distribution (“ΔT(x,y,z)”).
 12. Themethod as claimed in claim 1, wherein in the second calculation step (Hcontroller”, “fast H controller”), the temperature distribution that isto be expected is calculated by means of power-absorption-based scaling(“K”) of a calculated or provided relative temperature incrementdistribution (“ΔT(x,y,z)”).
 13. The method as claimed in claim 12,wherein the second calculation step (“H controller”, “fast Hcontroller”) has the following steps: calculation of a relative powerabsorption density distribution (“ΔSAR(x,y,z)”) and a relative averagepower absorption density (“ΔSAR_aver”) on the basis of a measuredgeometric distribution of the nanoparticles; provision of a basaltemperature distribution (“T0(x,y,z)”) on the basis of a numericalsolution to a bioheat transfer equation describing the model withoutpower absorption, and provision of a relative temperature incrementdistribution (“ΔT(x,y,z)”) on the basis of a numerical solution to thebioheat transfer equation with the calculated relative power absorptiondensity distribution (“ΔSAR(x,y,z)”); performance of the following stepsfor each field strength value from the plurality of prescribed fieldstrength values and/or the user-defined field strength value:calculation of a reference power absorption rate (“SAR_Fe”) whichindicates the specific power absorption rate of an undiluted magneticfluid containing the nanoparticles, for example, wherein the calculationis based on the respective field strength value (“H”) and a prescribedcharacteristic curve which relates to a relationship between referencepower absorption rate (“SAR_Fe”) and applied field strength (“H”);calculation, on the basis of the reference power absorption rate(“SAR_Fe”) and the calculated mass of the nanoparticles in the depositvolume, of an average power absorption density (“SAR_aver”); calculationof a power-absorption-based scaling factor (“K”) on the basis of therespective average power absorption density (“SAR_aver”) and therelative average power absorption density (“ΔSAR_aver”); calculation ofa respective temperature distribution (“T(x,y,z)”) on the basis of thebasal temperature distribution (“T0(x,y,z)”), the relative temperatureincrement distribution (“ΔT(x,y,z)”) and the power-absorption-basedscaling factor (“K”).
 14. A computer-aided simulation method (“Tselection”) for providing assistance in thermotherapy planning, whereinthe thermotherapy comprises hyperthermia treatment of a tumor volume ina body volume of a human body, wherein the hyperthermia treatmentcomprises the application of a magnetic field in a treatment volume bymeans of a magnetic field applicator, wherein thermal energy can beintroduced into at least one deposit volume by power absorption in theapplied magnetic field by means of magnetic, paramagnetic and/orsuperparamagnetic nanoparticles deposited in the body, wherein themethod relates to the calculation of a field strength that needs to beset on the applicator on the basis of a geometric distribution of thenanoparticles and at least one prescribed temperature limit value whichis not meant to be exceeded by the hyperthermia treatment (“Tselection”); and wherein the field strength value is calculated on thebasis of a prescribed characteristic curve which indicates arelationship between (reference) power absorption rate and fieldstrength.
 15. The method as claimed in claim 14, wherein the method hasthe following steps (“T selection”): calculation of an average powerabsorption density (“SAR_aver”) in the applicator magnetic field in thedeposit volume, wherein a relative power absorption density(“ΔSAR(x,y,z)”) is calculated on the basis of a measured geometricdistribution of the nanoparticles, a bioheat transfer equationdescribing the model is numerically solved precisely once in order toobtain a basal temperature distribution (“T0(x,y,z)”) without powerabsorption, and the bioheat transfer equation is numerically solvedprecisely once in order to obtain a relative temperature incrementdistribution (“ΔT(x,y,z)”) on the basis of the relative power absorptiondensity; and wherein the relative power absorption density(“ΔSAR(x,y,z)”) is scaled by a temperature-based scaling factor (“K”)which is obtained on the basis of the at least one prescribedtemperature limit value, the basal temperature distribution and therelative temperature increment distribution; calculation, on the basisof the calculated average power absorption density and the calculatedmass of the nanoparticles, of a reference power absorption rate(“SAR_Fe”) which indicates the specific power absorption rate of anundiluted magnetic fluid containing the nanoparticles, for example;calculation of a field strength value (“H”) on the basis of thecalculated reference power absorption rate (“SAR_Fe”) and a prescribedcharacteristic curve which relates to a relationship between referencepower absorption rate (“SAR_Fe”) and applied field strength (“H”);optional calculation of a respective temperature distribution(“T(x,y,z)”) on the basis of the basal temperature distribution(“T0(x,y,z)”), the relative temperature increment distribution(“ΔT(x,y,z)”) and the temperature-based scaling factor (“K”); provisionof the calculated field strength value (“H”) in order to provideassistance for the user in planning the thermotherapy; and optionalprovision of the calculated temperature distribution (“T(x,y,z)”) inorder to provide assistance for the user in planning the thermotherapy.16. A computer-aided simulation method (“H controller”, “fast Hcontroller”) for providing assistance in thermotherapy planning, whereinthe thermotherapy comprises hyperthermia treatment of a tumor volume ina body volume of a human body, wherein the hyperthermia treatmentcomprises the application of a magnetic field in a treatment volume bymeans of a magnetic field applicator, wherein thermal energy can beintroduced into at least one deposit volume by power absorption in theapplied magnetic field by means of magnetic, paramagnetic and/orsuperparamagnetic nanoparticles deposited in the body, wherein themethod relates to the calculation, for each field strength value from aplurality of prescribed field strength values and/or a user-definedfield strength value, of a temperature distribution that is to beexpected for at least some of the body volume; and wherein thetemperature distribution that is to be expected is calculated by meansof power-absorption-based scaling (“K”) of a calculated or providedrelative temperature increment distribution (“ΔT(x,y,z)”).
 17. Themethod as claimed in claim 16, wherein, regardless of the number ofprescribed and/or user-defined field strength values, a provided(previously calculated in the T selection step) basal temperaturedistribution (“T0(x,y,z)”) and/or a provided (previously calculated inthe T selection step) relative temperature increment distribution(“ΔT(x,y,z)”) is/are used (“fast H controller”), preferably, wherein nomore than two temperature distributions are calculated (“H controller”),namely a basal temperature distribution (“T0(x,y,z)”) and/or a relativetemperature increment distribution (“ΔT(x,y,z)”).
 18. The method asclaimed in claim 16, wherein the method has the following steps (“Hcontroller”, “fast H controller”): calculation of a relative powerabsorption density distribution (“ΔSAR(x,y,z)”) and a relative averagepower absorption density (“ΔSAR_aver”) on the basis of a measuredgeometric distribution of the nanoparticles; provision of a basaltemperature distribution (“T0(x,y,z)”) on the basis of a numericalsolution to a bioheat transfer equation describing the model withoutpower absorption, and provision of a relative temperature incrementdistribution (“ΔT(x,y,z)”) on the basis of a numerical solution to thebioheat transfer equation with the calculated relative power absorptiondensity (“ΔSAR(x,y,z)”); performance of the following steps for eachfield strength value from the plurality of prescribed field strengthvalues and/or the user-defined field strength value: calculation of areference power absorption rate (“SAR_Fe”) which indicates the specificpower absorption rate of an undiluted magnetic fluid containing thenanoparticles, for example, wherein the calculation is based on therespective field strength value (“H”) and a prescribed characteristiccurve which relates to a relationship between reference power absorptionrate (“SAR_Fe”) and applied field strength (“H”); calculation, on thebasis of the reference power absorption rate (“SAR_Fe”) and thecalculated mass of the nanoparticles in the deposit volume, of anaverage power absorption density (“SAR_aver”); calculation of apower-absorption-based scaling factor (“K”) on the basis of therespective average power absorption density (“SAR_aver”) and therelative power absorption density (“ΔSAR_aver”); calculation of arespective temperature distribution (“T(x,y,z)”) on the basis of thebasal temperature distribution (“T0(x,y,z)”), the relative temperatureincrement distribution (“ΔT(x,y,z)”) and the power-absorption-basedscaling factor (“K”); provision of the calculated temperaturedistributions in order to provide assistance for the user in planningthe thermotherapy.
 19. A computer program for carrying out the method asclaimed in claim 1, 10, or 12, when the computer program is executed ona programmable computer device.
 20. A computer device designed forproviding assistance in thermotherapy planning (“T selection”), whereinthe thermotherapy comprises hyperthermia treatment of a tumor volume ina body volume of a human body, wherein the hyperthermia treatmentcomprises the application of a magnetic field in the treatment volume bymeans of a magnetic field applicator, wherein thermal energy can beintroduced into at least one deposit volume by power absorption in theapplied magnetic field by means of magnetic, paramagnetic and/orsuperparamagnetic nanoparticles deposited in the body, wherein thecomputer device has a component (“sim_t.exe”,“mainsubroutine_sim_t_voxel_win”) which is designed to calculate a fieldstrength that needs to be set on the applicator on the basis of ageometric distribution of the nanoparticles and at least one prescribedtemperature limit value which is not meant to be exceeded by thehyperthermia treatment; and wherein the component (“sim_t.exe”,“mainsubroutine_sim_t_voxel_win”) has a module for calculating the fieldstrength value on the basis of a prescribed characteristic curve,wherein the characteristic curve indicates a relationship between powerabsorption rate and field strength.
 21. The computer device as claimedin claim 20, wherein the computer device has the following module (“Tselection”): a module for calculating an average power absorptiondensity in the applicator magnetic field in the deposit volume, whereina relative power absorption density is calculated on the basis of ameasured geometric distribution of the nanoparticles, a bioheat transferequation describing the model is numerically solved precisely once inorder to obtain a basal temperature distribution without powerabsorption, and the bioheat transfer equation is numerically solvedprecisely once in order to obtain a relative temperature incrementdistribution on the basis of the relative power absorption density; andwherein the relative power absorption density is scaled by a scalingfactor which is obtained on the basis of the at least one prescribedtemperature limit value, the basal temperature distribution and therelative temperature increment distribution (“temperature-based scalingfactor”); a module for calculating, on the basis of the calculatedaverage power absorption density and the calculated mass of thenanoparticles, a reference power absorption rate which indicates thespecific power absorption rate of an undiluted magnetic fluid containingthe nanoparticles, for example; a module for calculating a fieldstrength value on the basis of the calculated reference power absorptionrate and a prescribed characteristic curve which relates to arelationship between reference power absorption rate and applied fieldstrength; a module for providing the calculated field strength value inorder to provide assistance for the user in planning the thermotherapy,optionally a module for calculating respective temperature distributionon the basis of the basal temperature distribution, the relativetemperature increment distribution and the temperature-based scalingfactor; and optionally a module for providing the calculated temperaturedistributions in order to provide assistance for the user in planningthe thermotherapy.
 22. A computer device designed for providingassistance in thermotherapy planning (“H controller”, “fast Hcontroller”), wherein the thermotherapy comprises hyperthermia treatmentof a tumor volume in a body volume of a human body, wherein thehyperthermia treatment comprises the application of a magnetic field inthe treatment volume by means of a magnetic field applicator, whereinthermal energy can be introduced into at least one deposit volume bypower absorption in the applied magnetic field by means of magnetic,paramagnetic and/or superparamagnetic nanoparticles deposited in thebody, wherein the computer device has a component (“sim_hr.exe”,“mainsubroutine_sim_hr_voxel_win”) which is designed to calculate, foreach field strength value from a plurality of prescribed field strengthvalues and/or a user-defined field strength value, a temperaturedistribution that is to be expected for at least some of the bodyvolume; and wherein the component (“sim_hr.exe”,“mainsubroutine_sim_hr_voxel_win”) has a module for calculating thetemperature distribution that is to be expected by means ofpower-absorption-based scaling (“K”) of a calculated or providedtemperature increment distribution (“ΔT(x,y,z)”).
 23. The computerdevice as claimed in claim 22, wherein the component(“mainsubroutine_sim_hr_voxel_win”) is designed to calculate thetemperature distribution that is to be expected in order to use,regardless of the number of prescribed and/or user-defined fieldstrength values, a provided (previously calculated inmainsubroutine_sim_t_voxel_win) basal temperature distribution(“T0(x,y,z)”) and/or a provided (previously calculated inmainsubroutine_sim_t_voxel_win) relative temperature incrementdistribution (“ΔT(x,y,z)”) (“fast H controller”).
 24. The computerdevice as claimed in claim 23, wherein the component (“sim_hr.exe”) forcalculating the temperature distribution that is to be expected isdesigned to calculate, regardless of the number of prescribed and/oruser-defined field strength values, no more than two temperaturedistributions (“H controller”), namely a basal temperature distribution(“T0(x,y,z)”) and/or a relative temperature increment distribution(“ΔT(x,y,z)”).
 25. The computer device as claimed in claim 24, whereinthe computer device has the following modules (“H controller”, “fast Hcontroller”): a module for calculating a relative power absorptiondensity distribution and a relative average power absorption density onthe basis of a measured geometric distribution of the nanoparticles; amodule for providing a basal temperature distribution on the basis of anumerical solution to a bioheat transfer equation describing the modelwithout power absorption, and providing a relative temperature incrementdistribution on the basis of a numerical solution to the bioheattransfer equation with the calculated relative power absorption densitydistribution; a module for performing the following steps for each fieldstrength value from the plurality of prescribed field strength valuesand/or the user-defined field strength value: calculation of a referencepower absorption rate which indicates the specific power absorption rateof an undiluted magnetic fluid containing the nanoparticles, forexample, wherein the calculation is based on the respective fieldstrength value and a prescribed characteristic curve which relates to arelationship between reference power absorption rate and applied fieldstrength; calculation, on the basis of the reference power absorptionrate and the calculated mass of the nanoparticles in the deposit volume,of an average power absorption density; calculation of apower-absorption-based scaling factor on the basis of the respectiveaverage power absorption density and the relative power absorptiondensity; calculation of a respective temperature distribution on thebasis of the basal temperature distribution, the relative temperatureincrement distribution and the power-absorption-based scaling factor; amodule for providing the calculated temperature distributions in orderto provide assistance for the user in planning the thermotherapy.
 26. Asystem, comprising a computer device as claimed in one of claim 20, or22 and a magnetic field applicator.
 27. A system, comprising a computerprogram for carrying out the method as claimed in one of claim 1, 14 or16, when the computer program is executed on a programmable computerdevice, a computer device designed for providing assistance inthermotherapy planning (“T selection”), wherein the thermotherapycomprises hyperthermia treatment of a tumor volume in a body volume of ahuman body, wherein the hyperthermia treatment comprises the applicationof a magnetic field in the treatment volume by means of a magnetic fieldapplicator, wherein thermal energy can be introduced into at least onedeposit volume by power absorption in the applied magnetic field bymeans of magnetic, paramagnetic and/or superparamagnetic nanoparticlesdeposited in the body, wherein the computer device has a component(“sim_t.exe”, “mainsubroutine_sim_t_voxel_win”) which is designed tocalculate a field strength that needs to be set on the applicator on thebasis of a geometric distribution of the nanoparticles and at least oneprescribed temperature limit value which is not meant to be exceeded bythe hyperthermia treatment; and wherein the component (“sim_t.exe”,“mainsubroutine_sim_t_voxel_win”) has a module for calculating the fieldstrength value on the basis of a prescribed characteristic curve,wherein the characteristic curve indicates a relationship between powerabsorption rate and field strength, or a system comprising such acomputer device and a magnetic field applicator, and also comprising amagnetic fluid containing magnetic nano-particles.
 28. A system,comprising a computer program for carrying out the method as claimed inone of claim 1, 14 or 16, when the computer program is executed on aprogrammable computer device, a computer device designed for providingassistance in thermotherapy planning (“H controller”, “fast Hcontroller”), wherein the thermotherapy comprises hyperthermia treatmentof a tumor volume in a body volume of a human body, wherein thehyperthermia treatment comprises the application of a magnetic field inthe treatment volume by means of a magnetic field applicator, whereinthermal energy can be introduced into at least one deposit volume bypower absorption in the applied magnetic field by means of magnetic,paramagnetic and/or superparamagnetic nanoparticles deposited in thebody, wherein the computer device has a component (“sim_hr.exe”,“mainsubroutine_sim_hr_voxel_win”) which is designed to calculate, foreach field strength value from a plurality of prescribed field strengthvalues and/or a user-defined field strength value, a temperaturedistribution that is to be expected for at least some of the bodyvolume; and wherein the component (“sim_hr.exe”,“mainsubroutine_sim_hr_voxel_win”) has a module for calculating thetemperature distribution that is to be expected by means ofpower-absorption-based scaling (“K”) of a calculated or providedtemperature increment distribution (“ΔT(x,y,z)”), or a system comprisingsuch a computer device and a magnetic field applicator, and alsocomprising a magnetic fluid containing magnetic nano-particles.